diff --git a/404.html b/404.html index aaee9c54..cb41d14d 100644 --- a/404.html +++ b/404.html @@ -18,7 +18,7 @@ - +
- +
@@ -103,16 +103,16 @@

Page not found (404)

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html index 7ec2901c..782bd9ab 100644 --- a/CODE_OF_CONDUCT.html +++ b/CODE_OF_CONDUCT.html @@ -3,7 +3,7 @@ - +
- +
@@ -139,15 +139,15 @@

Attribution -

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html index 68c40100..0540bcbe 100644 --- a/CONTRIBUTING.html +++ b/CONTRIBUTING.html @@ -3,7 +3,7 @@ - +
- +
@@ -105,15 +105,15 @@

Exception to the coding style -

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/LICENSE-text.html b/LICENSE-text.html index c7367f0f..d85b2a7c 100644 --- a/LICENSE-text.html +++ b/LICENSE-text.html @@ -3,7 +3,7 @@ - +
- +
@@ -356,15 +356,15 @@

License

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/articles/demo.html b/articles/demo.html index 1dc2c9e7..730e8d64 100644 --- a/articles/demo.html +++ b/articles/demo.html @@ -12,14 +12,13 @@ - - +
- +
@@ -86,7 +85,7 @@

Niek Den Teuling

- Source: vignettes/demo.Rmd + Source: vignettes/demo.Rmd
@@ -146,14 +145,16 @@

Non-parametric trajectory clusteri

Assuming the appropriate (cluster) trajectory model is not known in advance, non-parametric longitudinal cluster models can provide a suitable starting point.

-

As an example, we apply longitudinal \(k\)-means (KML). First, we need to define -the method. At the very least we need to indicate the response variable -the method should operate on. Secondly, we should indicate how many -clusters we expect. We do not need to define the id and -time arguments as we have set these as package options. We -use the nbRedrawing argument provided by the KML package -for reducing the number of repeated random starts to only a single model -estimation, in order to reduce the run-time of this example.

+

As an example, we apply longitudinal +kk-means +(KML). First, we need to define the method. At the very least we need to +indicate the response variable the method should operate on. Secondly, +we should indicate how many clusters we expect. We do not need to define +the id and time arguments as we have set these +as package options. We use the nbRedrawing argument +provided by the KML package for reducing the number of repeated random +starts to only a single model estimation, in order to reduce the +run-time of this example.

 kmlMethod <- lcMethodKML(response = "Y", nClusters = 2, nbRedrawing = 1)
 
@@ -360,9 +361,8 @@ 
Investigating the preferred model#> 3  3       D
 #> 4  4       C
 #> 5  5       D
-#> 6  6       D
-
-
+#> 6  6       D
+
 posthocAnalysisData = merge(latrendData, subjectClusters, by = 'Id')
 head(posthocAnalysisData)
 #>   Id      Time           Y   Class Cluster
@@ -371,9 +371,8 @@ 
Investigating the preferred model#> 3  1 0.4444444 -0.65148373 Class 1       D
 #> 4  1 0.6666667 -0.39115398 Class 1       D
 #> 5  1 0.8888889 -0.19407876 Class 1       D
-#> 6  1 1.1111111 -0.02991783 Class 1       D
-
-aggregate(Y ~ Cluster, posthocAnalysisData, mean)
+#> 6  1 1.1111111 -0.02991783 Class 1       D
+aggregate(Y ~ Cluster, posthocAnalysisData, mean)
 #>   Cluster           Y
 #> 1       A -0.01200153
 #> 2       B -0.05675290
@@ -386,7 +385,7 @@ 
Model adequacyThe list of currently supported internal model metrics can be
 obtained by calling the getInternalMetricNames
 function.

-
+
 getInternalMetricNames()
 #>  [1] "AIC"              "APPA.mean"        "APPA.min"         "ASW"             
 #>  [5] "BIC"              "CAIC"             "CalinskiHarabasz" "CLC"             
@@ -400,13 +399,13 @@ 
Model adequacyAs an example, we will compute the APPA (a measure of cluster
 separation), and the WRSS and WMAE metrics (measures of model
 error).

-
+
 metric(kmlModel, c("APPA.mean", "WRSS", "WMAE"))
 #>   APPA.mean        WRSS        WMAE 
 #>   0.9999979 152.7557738   0.2158131

 
The quantile-quantile (QQ) plot can be used to assess the model for
 structural deviations.

-
+
 qqPlot(kmlModel4)

 

 

@@ -418,7 +417,7 @@ 
Model adequacy
-
+
 qqPlot(kmlModel4, byCluster = TRUE, detrend = TRUE)

 

 

@@ -435,7 +434,7 @@ 
Parametric trajectory clustering
-
+
 lmkmMethod <- lcMethodLMKM(formula = Y ~ Time)
 
 lmkmMethod
@@ -456,11 +455,11 @@ 
Parametric trajectory clustering#>  algorithm:      c("Hartigan-Wong", "Lloyd", "Forgy", "Ma
 #>  formula:        Y ~ Time

 
We fit LMKM for 1 to 4 clusters.

-
+
 lmkmMethods <- lcMethods(lmkmMethod, nClusters = 1:5)
 
 lmkmModels <- latrendBatch(lmkmMethods, data = latrendData, verbose = FALSE)

-
+
 plotMetric(lmkmModels, c("logLik", "BIC", "WMAE"))

 

 

@@ -469,7 +468,7 @@ 
Parametric trajectory clustering
 

 
All metrics clearly point to the three-cluster solution.

-
+
 bestLmkmModel <- subset(lmkmModels, nClusters == 3, drop=TRUE)
 plot(bestLmkmModel)

 

@@ -484,7 +483,7 @@ 
Comparison to the reference
+
 plotClusterTrajectories(latrendData, response = "Y", cluster = "Class")

 

 

@@ -497,8 +496,8 @@ 
Comparison to the reference
-refTrajAssigns <- aggregate(Class ~ Id, data = latrendData, FUN = data.table::first)
+
+refTrajAssigns <- aggregate(Class ~ Id, data = latrendData, FUN = data.table::first)
 refModel <- lcModelPartition(data = latrendData, response = "Y", trajectoryAssignments = refTrajAssigns$Class)
 refModel
 #> Longitudinal cluster model using part
@@ -514,7 +513,7 @@ 
Comparison to the reference#> Scaled residuals:
 #>     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 #> -3.67125 -0.55452 -0.04079  0.00000  0.58663  3.76931

-
+
 plot(refModel)

 

 

@@ -526,7 +525,7 @@ 
Comparison to the reference
+
 getExternalMetricNames()
 #>  [1] "adjustedRand"   "CohensKappa"    "F"              "F1"            
 #>  [5] "FolkesMallows"  "Hubert"         "Jaccard"        "jointEntropy"  
@@ -540,7 +539,7 @@ 
Comparison to the reference#> [37] "WMMSE.ref"      "WMRSS"          "WMRSS.ref"

 
Lastly, we compare the agreement in trajectory assignments via the
 adjusted Rand index.

-
+
 externalMetric(bestLmkmModel, refModel, "adjustedRand")
 #> adjustedRand 
 #>    0.9669189

@@ -553,9 +552,7 @@ 
Comparison to the reference
 
-        
-

+      

 
 

 
@@ -568,16 +565,16 @@ 
Comparison to the reference
   

-
Site built with pkgdown 2.0.9.

+
Site built with pkgdown 2.1.1.

 

 
       
 

 
-  
 
 
-  
+
+
 
   
 
diff --git a/articles/implement.html b/articles/implement.html
index 1bb66494..c51dc4f5 100644
--- a/articles/implement.html
+++ b/articles/implement.html
@@ -12,14 +12,13 @@
 
 
 
-
 
 
 
-    
+
 
     

       

 
 
-      
+
 
       

   

@@ -86,7 +85,7 @@ 
Niek Den
 Teuling

             
       
-      Source: vignettes/implement.Rmd
+      Source: vignettes/implement.Rmd
       
 
     

@@ -127,7 +126,7 @@ 
Case study data  randomScales = cbind(c(.2, .02), c(.2, .02)),
   noiseScales = .05
 ) %>% 
-  as.data.table()

+  as.data.table()

 
We plot the data to get a sense of different trajectories that have
 been generated. Visually, there is little group separation.

 
@@ -211,7 +210,7 @@ 
Two-step clustering
 repStep <- function(method, data, verbose) {
-  dt <- as.data.table(data)
+  dt <- as.data.table(data)
   coefdata <- dt[, lm(Y ~ Time, .SD) %>% coef() %>% as.list(), keyby = Traj]
   coefmat <- subset(coefdata, select = -1) %>% as.matrix()
   rownames(coefmat) <- coefdata$Traj
@@ -219,8 +218,9 @@ 
Two-step clustering}

 
The cluster step takes the coefficient matrix as input. A
 cross-sectional cluster algorithm can then be applied to the matrix. In
-this example, we apply \(k\)-means. The
-cluster step should output a lcModel object.

+this example, we apply
+kk-means.
+The cluster step should output a lcModel object.

 
The lcModelPartition function creates a
 lcModel object for a given vector of cluster
 assignments.

@@ -280,7 +280,7 @@ 
General approach
 
 repStep.gen <- function(method, data, verbose) {
-  dt <- as.data.table(data)
+  dt <- as.data.table(data)
   coefdata <- dt[, lm(method$formula, .SD) %>% coef() %>% as.list(), keyby = c(method$id)]
   # exclude the id column
   coefmat <- subset(coefdata, select = -1) %>% as.matrix()
@@ -343,9 +343,7 @@ 
General approach
 
-        
-

+      

 
 

 
@@ -358,16 +356,16 @@ 
General approach
   

-
Site built with pkgdown 2.0.9.

+
Site built with pkgdown 2.1.1.

 

 
       
 

 
-  
 
 
-  
+
+
 
   
 
diff --git a/articles/index.html b/articles/index.html
index 12099509..bf3b90a4 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

@@ -79,15 +79,15 @@ 
All vignettes

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/articles/simulation.html b/articles/simulation.html
index f72faf9e..703f9fb5 100644
--- a/articles/simulation.html
+++ b/articles/simulation.html
@@ -12,14 +12,13 @@
 
 
 
-
 
 
 
-    
+
 
     

       

 
 
-      
+
 
       

   

@@ -86,7 +85,7 @@ 
Niek Den
 Teuling

             
       
-      Source: vignettes/simulation.Rmd
+      Source: vignettes/simulation.Rmd
       
 
     

@@ -124,24 +123,36 @@ 
Data generation
 
For demonstration purposes, we define a trivial dataset comprising
 two distinct groups with group trajectories represented by a line (i.e.,
-intercept and slope). A trajectory \(\textbf{y}_i\) belonging to group \(k\) is described by \(y_{ij} = \beta_{0k} + \beta_{1k} t_{j} + z_{0i} +
-e_{ij}\). Here, \(\beta_{0k}\)
-and \(\beta_{1k}\) are the intercept
-and slope for group \(k\),
-respectively. Furthermore, \(z_i\)
+intercept and slope). A trajectory
+𝐲i\textbf{y}_i
+belonging to group
+kk
+is described by
+yij=β0k+β1ktj+z0i+eijy_{ij} = \beta_{0k} + \beta_{1k} t_{j} + z_{0i} + e_{ij}.
+Here,
+β0k\beta_{0k}
+and
+β1k\beta_{1k}
+are the intercept and slope for group
+kk,
+respectively. Furthermore,
+ziz_i
 denotes the trajectory-specific random intercept, i.e., its deviation
-from the group trajectory. Lastly, \(e_{ij}\) represents independent random
-noise with \(e_{ij} \sim N(0,
-\sigma^2)\).

+from the group trajectory. Lastly,
+eije_{ij}
+represents independent random noise with
+eij∼N(0,σ2)e_{ij} \sim N(0, \sigma^2).

 
We can generate data according to this model using a utility function
 named generateLongData() that is included in the package.
 This function generates datasets based on a mixture of linear mixed
 models. We create a wrapper around this function in order to adapt the
 function to our needs. Most importantly, we code the shape and
-coefficients of the group trajectories as fixed, setting \((\beta_{0A},\beta_{1A}) = (0, 0)\) for
-group A (40%) and \((\beta_{0B},\beta_{1B}) =
-(1, -1)\) for group B (60%). Other data settings (e.g., the
-magnitude of perturbation) are passed via ....

+coefficients of the group trajectories as fixed, setting
+(β0A,β1A)=(0,0)(\beta_{0A},\beta_{1A}) = (0, 0)
+for group A (40%) and
+(β0B,β1B)=(1,−1)(\beta_{0B},\beta_{1B}) = (1, -1)
+for group B (60%). Other data settings (e.g., the magnitude of
+perturbation) are passed via ....

 
+#> Estimated replications per hour: 259
+#> Start of the simulation: 2024-11-15 09:11:12.114215
+#> End of the simulation: 2024-11-15 09:11:25.962403

 

 

 
Analyzing the results
@@ -350,7 +361,7 @@ 
Analyzing the results
 
 library(data.table)
-resultsTable <- as.data.table(result$simulation)

+resultsTable <- as.data.table(result$simulation)

 

 
Recovery of the number of clusters
 

@@ -482,9 +493,7 @@ 
Residual error
 
-        
-

+

@@ -497,16 +506,16 @@

Residual error

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/authors.html b/authors.html index b1ee87a7..46dde52c 100644 --- a/authors.html +++ b/authors.html @@ -3,7 +3,7 @@ - +
- +
@@ -60,35 +60,35 @@

Authors and Citation

- +

Citation

- Source: DESCRIPTION + Source: DESCRIPTION

Den Teuling N (2024). latrend: A Framework for Clustering Longitudinal Data. -R package version 1.6.1, https://philips-software.github.io/latrend/, https://github.com/philips-software/latrend. +R package version 1.6.1, https://philips-software.github.io/latrend/, https://github.com/philips-software/latrend. 

@Manual{,
   title = {latrend: A Framework for Clustering Longitudinal Data},
@@ -109,15 +109,15 @@ 
Citation

 
 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       
 
-  
 
 
-  
+
+
 
   
 
diff --git a/index.html b/index.html
index 9e2a2769..44999500 100644
--- a/index.html
+++ b/index.html
@@ -19,7 +19,7 @@
 
 
 
-    
+
 
     

       

 
 
-      
+
 
       

   

@@ -225,16 +225,16 @@ 
Dev status

 
 

   

-
Site built with pkgdown 2.0.9.

+
Site built with pkgdown 2.1.1.

 

 
       
 

 
-  
 
 
-  
+
+
 
   
 
diff --git a/pkgdown.yml b/pkgdown.yml
index 633c07bc..80c47617 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,9 +1,8 @@
 pandoc: 3.1.11
-pkgdown: 2.0.9
+pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
   demo: demo.html
   implement: implement.html
   simulation: simulation.html
-last_built: 2024-05-15T11:39Z
-
+last_built: 2024-11-15T09:09Z
diff --git a/reference/APPA.html b/reference/APPA.html
index 7f6198d2..6872cf50 100644
--- a/reference/APPA.html
+++ b/reference/APPA.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Average posterior probability of assignment (APPA)

-    Source: R/model-evaluation.R
+    Source: R/model-evaluation.R
     
     

 
@@ -71,32 +71,22 @@ 
Average posterior probability of assignment (APPA)

 
     

     
Arguments

-    
object

+
+
+
object

 
The model, of type lcModel.

 
 

     

     
Value

-    
-
-
The APPA per cluster, as a numeric vector of length nClusters(object).
+    
The APPA per cluster, as a numeric vector of length nClusters(object).
 Empty clusters will output NA.

     

     

     
References

-    
Nagin DS (2005).
-Group-based modeling of development.
-Harvard University Press.
-ISBN 9780674041318, doi:10.4159/9780674041318
-.

-
Klijn SL, Weijenberg MP, Lemmens P, van den Brandt PA, Passos VL (2017).
-“Introducing the fit-criteria assessment plot - A visualisation tool to assist class enumeration in group-based trajectory modelling.”
-Statistical Methods in Medical Research, 26(5), 2424-2436.

-
van der Nest G, Lima Passos V, Candel MJ, van Breukelen GJ (2020).
-“An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software.”
-Advances in Life Course Research, 43, 100323.
-ISSN 1040-2608, doi:10.1016/j.alcr.2019.100323
-.

+    
nagin2005grouplatrend

+
klijn2017introducinglatrend

+
vandernest2020overviewlatrend

     

     

     
See also

@@ -115,15 +105,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/OCC.html b/reference/OCC.html
index 1dbf22df..bea90b7f 100644
--- a/reference/OCC.html
+++ b/reference/OCC.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Odds of correct classification (OCC)

-    Source: R/model-evaluation.R
+    Source: R/model-evaluation.R
     
     

 
@@ -73,15 +73,15 @@ 
Odds of correct classification (OCC)

 
     

     
Arguments

-    
object

+
+
+
object

 
The model, of type lcModel.

 
 

     

     
Value

-    
-
-
The OCC per cluster, as a numeric vector of length nClusters(object).
+    
The OCC per cluster, as a numeric vector of length nClusters(object).
 Empty clusters will output NA.

     

     

@@ -90,21 +90,9 @@ 
Details

     

     

     
References

-    
Nagin DS (2005).
-Group-based modeling of development.
-Harvard University Press.
-ISBN 9780674041318, doi:10.4159/9780674041318
-.
-
-Klijn SL, Weijenberg MP, Lemmens P, van den Brandt PA, Passos VL (2017).
-“Introducing the fit-criteria assessment plot - A visualisation tool to assist class enumeration in group-based trajectory modelling.”
-Statistical Methods in Medical Research, 26(5), 2424-2436.
-
-van der Nest G, Lima Passos V, Candel MJ, van Breukelen GJ (2020).
-“An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software.”
-Advances in Life Course Research, 43, 100323.
-ISSN 1040-2608, doi:10.1016/j.alcr.2019.100323
-.

+    
nagin2005grouplatrend
+klijn2017introducinglatrend
+vandernest2020overviewlatrend

     

     

     
See also

@@ -123,15 +111,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/PAP.adh-1.png b/reference/PAP.adh-1.png
index 21f68674..6ef568d8 100644
Binary files a/reference/PAP.adh-1.png and b/reference/PAP.adh-1.png differ
diff --git a/reference/PAP.adh.html b/reference/PAP.adh.html
index 5676fa38..857ba272 100644
--- a/reference/PAP.adh.html
+++ b/reference/PAP.adh.html
@@ -6,7 +6,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Weekly Mean PAP Therapy Usage of OSA Patients in the First 3 Months

-    Source: R/data.R
+    Source: R/data.R
     
     

 
@@ -93,11 +93,7 @@ 
Format

 
factor: The reference group (i.e., adherence pattern) from which this patient was generated.

 
 
-
Yi H, Dong X, Shang S, Zhang C, Xu L, Han F (2022).
-“Identifying longitudinal patterns of CPAP treatment in OSA using growth mixture modeling: Disease characteristics and psychological determinants.”
-Frontiers in Neurology, 13, 1063461.
-doi:10.3389/fneur.2022.1063461
-.

+
yi2022identifyinglatrend

     

     

     
See also

@@ -136,15 +132,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/PAP.adh1y-1.png b/reference/PAP.adh1y-1.png
index 6618f1a0..520552a2 100644
Binary files a/reference/PAP.adh1y-1.png and b/reference/PAP.adh1y-1.png differ
diff --git a/reference/PAP.adh1y.html b/reference/PAP.adh1y.html
index 6a3dcc22..9753fabf 100644
--- a/reference/PAP.adh1y.html
+++ b/reference/PAP.adh1y.html
@@ -8,7 +8,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Biweekly Mean PAP Therapy Adherence of OSA Patients over 1 Year

-    Source: R/data.R
+    Source: R/data.R
     
     

 
@@ -105,11 +105,7 @@ 
Source

     
This dataset was generated based on the cluster-specific descriptive statistics table
 provided in Aloia et al. (2008),
 with some adjustments made in order to improve cluster separation for demonstration purposes.

-
Aloia MS, Goodwin MS, Velicer WF, Arnedt JT, Zimmerman M, Skrekas J, Harris S, Millman RP (2008).
-“Time series analysis of treatment adherence patterns in individuals with obstructive sleep apnea.”
-Annals of Behavioral Medicine, 36(1), 44--53.
-ISSN 0883-6612, doi:10.1007/s12160-008-9052-9
-.

+
aloia2008timelatrend

     

     

     
Note

@@ -152,15 +148,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/Rplot001.png b/reference/Rplot001.png
deleted file mode 100644
index 17a35806..00000000
Binary files a/reference/Rplot001.png and /dev/null differ
diff --git a/reference/Rplot002.png b/reference/Rplot002.png
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index 16efab55..00000000
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diff --git a/reference/as.data.frame.lcMethod.html b/reference/as.data.frame.lcMethod.html
index cc415361..818aee6c 100644
--- a/reference/as.data.frame.lcMethod.html
+++ b/reference/as.data.frame.lcMethod.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 
 
-      
+
 
       

   

     

     
Convert lcMethod arguments to a list of atomic types

-    Source: R/method.R
+    Source: R/method.R
     
     

 
@@ -66,41 +66,41 @@ 
Convert lcMethod arguments to a list of atomic types

     

 
     

-    
# S3 method for lcMethod
+    
# S3 method for class 'lcMethod'
 as.data.frame(x, ..., eval = TRUE, nullValue = NA, envir = NULL)

     

 
     

     
Arguments

-    
x

+
+
+
x

 
lcMethod to be coerced to a character vector.

 
 
-
...

+
...

 
Additional arguments.

 
 
-
eval

+
eval

 
Whether to evaluate the arguments in order to replace expression if the resulting value is of a class specified in evalClasses.

 
 
-
nullValue

+
nullValue

 
Value to use to represent the NULL type. Must be of length 1.

 
 
-
envir

+
envir

 
The environment in which to evaluate the arguments. If NULL, the environment associated with the object is used. If not available, the parent.frame() is used.

 
 

     

     
Value

-    
-
-
A single-row data.frame where each columns represents an argument call or evaluation.

+    
A single-row data.frame where each columns represents an argument call or evaluation.

     

     

     
See also

-    
Other lcMethod functions: 
+    
Other lcMethod functions:
 [[,lcMethod-method,
 as.data.frame.lcMethods(),
 as.lcMethods(),
@@ -124,15 +124,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/as.data.frame.lcMethods.html b/reference/as.data.frame.lcMethods.html
index 4cf48f55..5fdbc601 100644
--- a/reference/as.data.frame.lcMethods.html
+++ b/reference/as.data.frame.lcMethods.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Convert a list of lcMethod objects to a data.frame

-    Source: R/methods.R
+    Source: R/methods.R
     
     

 
@@ -66,41 +66,41 @@ 
Convert a list of lcMethod objects to a data.frame

     

 
     

-    
# S3 method for lcMethods
+    
# S3 method for class 'lcMethods'
 as.data.frame(x, ..., eval = TRUE, nullValue = NA, envir = parent.frame())

     

 
     

     
Arguments

-    
x

+
+
+
x

 
the lcMethods or list to be coerced to a data.frame.

 
 
-
...

+
...

 
Additional arguments.

 
 
-
eval

+
eval

 
Whether to evaluate the arguments in order to replace expression if the resulting value is of a class specified in evalClasses.

 
 
-
nullValue

+
nullValue

 
Value to use to represent the NULL type. Must be of length 1.

 
 
-
envir

+
envir

 
The environment in which to evaluate the arguments. If NULL, the environment associated with the object is used. If not available, the parent.frame() is used.

 
 

     

     
Value

-    
-
-
A data.frame with each row containing the argument values of a method object.

+    
A data.frame with each row containing the argument values of a method object.

     

     

     
See also

-    
Other lcMethod functions: 
+    
Other lcMethod functions:
 [[,lcMethod-method,
 as.data.frame.lcMethod(),
 as.lcMethods(),
@@ -124,15 +124,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/as.data.frame.lcModels.html b/reference/as.data.frame.lcModels.html
index 65e52a79..bb44f418 100644
--- a/reference/as.data.frame.lcModels.html
+++ b/reference/as.data.frame.lcModels.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Generate a data.frame containing the argument values per method per row

-    Source: R/models.R
+    Source: R/models.R
     
     

 
@@ -66,37 +66,37 @@ 
Generate a data.frame containing the argument values per method per row

     

 
     

-    
# S3 method for lcModels
+    
# S3 method for class 'lcModels'
 as.data.frame(x, ..., excludeShared = FALSE, eval = TRUE)

     

 
     

     
Arguments

-    
x

+
+
+
x

 
lcModels or a list of lcModel

 
 
-
...

+
...

 
Arguments passed to as.data.frame.lcMethod.

 
 
-
excludeShared

+
excludeShared

 
Whether to exclude columns which have the same value across all methods.

 
 
-
eval

+
eval

 
Whether to evaluate the arguments in order to replace expression if the resulting value is of a class specified in evalClasses.

 
 

     

     
Value

-    
-
-
A data.frame.

+    
A data.frame.

     

     

     
Functionality

-    
+
 
 
  • Print an argument summary for each of the models.
    • 
 
  • Convert to a data.frame of method arguments.
    • 
@@ -120,15 +120,15 @@ 
    Functionality
    
 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/as.lcMethods.html b/reference/as.lcMethods.html
index b48d8933..b6e4dfd0 100644
--- a/reference/as.lcMethods.html
+++ b/reference/as.lcMethods.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Convert a list of lcMethod objects to a lcMethods list

-    Source: R/methods.R
+    Source: R/methods.R
     
     

 
@@ -71,19 +71,19 @@ 
Convert a list of lcMethod objects to a lcMethods list

 
     

     
Arguments

-    
x

+
+
+
x

 
A list of lcMethod objects.

 
 

     

     
Value

-    
-
-
A lcMethods object.

+    
A lcMethods object.

     

     

     
See also

-    
Other lcMethod functions: 
+    
Other lcMethod functions:
 [[,lcMethod-method,
 as.data.frame.lcMethod(),
 as.data.frame.lcMethods(),
@@ -107,15 +107,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/as.lcModels.html b/reference/as.lcModels.html
index 8b13b3db..f53f25ec 100644
--- a/reference/as.lcModels.html
+++ b/reference/as.lcModels.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Convert a list of lcModels to a lcModels list

-    Source: R/models.R
+    Source: R/models.R
     
     

 
@@ -71,19 +71,19 @@ 
Convert a list of lcModels to a lcModels list

 
     

     
Arguments

-    
x

+
+
+
x

 
A list of lcModel objects, an lcModels object, or NULL.

 
 

     

     
Value

-    
-
-
A lcModels object.

+    
A lcModels object.

     

     

     
Functionality

-    
+
 
 
  • Print an argument summary for each of the models.
    • 
 
  • Convert to a data.frame of method arguments.
    • 
@@ -97,7 +97,7 @@ 
    Functionality
    
     
    
     
    See also
    
     
    lcModels
    
-
    Other lcModels functions: 
+
    Other lcModels functions:
 lcModels,
 lcModels-class,
 max.lcModels(),
@@ -119,15 +119,15 @@ 
    See also
    
 
    
 
 
    
-  
    Site built with pkgdown 2.0.9.
    
+  
    Site built with pkgdown 2.1.1.
    
 
    
 
       
    
 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/as.list.lcMethod.html b/reference/as.list.lcMethod.html
index e350709e..83739340 100644
--- a/reference/as.list.lcMethod.html
+++ b/reference/as.list.lcMethod.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     
    
       
    
   
    
 

 
-      
+
 
       

   

     

     
Extract the method arguments as a list

-    Source: R/method.R
+    Source: R/method.R
     
     

 
@@ -66,46 +66,46 @@ 
Extract the method arguments as a list

     

 
     

-    
# S3 method for lcMethod
+    
# S3 method for class 'lcMethod'
 as.list(x, ..., args = names(x), eval = TRUE, expand = FALSE, envir = NULL)

     

 
     

     
Arguments

-    
x

+
+
+
x

 
The lcMethod object.

 
 
-
...

+
...

 
Additional arguments.

 
 
-
args

+
args

 
A character vector of argument names to select. Only available arguments are returned.
 Alternatively, a function or list of functions, whose formal arguments will be selected from the method.

 
 
-
eval

+
eval

 
Whether to evaluate the arguments.

 
 
-
expand

+
expand

 
Whether to return all method arguments when "..." is present among the requested argument names.

 
 
-
envir

+
envir

 
The environment in which to evaluate the arguments. If NULL, the environment associated with the object is used. If not available, the parent.frame() is used.

 
 

     

     
Value

-    
-
-
A list with the argument calls or evaluated results depending on the value for eval.

+    
A list with the argument calls or evaluated results depending on the value for eval.

     

     

     
See also

-    
Other lcMethod functions: 
+    
Other lcMethod functions:
 [[,lcMethod-method,
 as.data.frame.lcMethod(),
 as.data.frame.lcMethods(),
@@ -136,13 +136,13 @@ 
Examples

 #> {
 #>     mean(x, ..., na.rm = TRUE)
 #> }
-#> <bytecode: 0x55f65284ada8>
+#> <bytecode: 0x5635f36a80b8>
 #> <environment: namespace:latrend>
 #> 
 #> $standardize
 #> function (x, center = TRUE, scale = TRUE) 
 #> UseMethod("scale")
-#> <bytecode: 0x55f6528773d8>
+#> <bytecode: 0x5635f36dcdd0>
 #> <environment: namespace:base>
 #> 
 #> $method
@@ -174,7 +174,7 @@ 
Examples

 #> 
 #> $formula
 #> Y ~ Time
-#> <environment: 0x55f652a754e8>
+#> <environment: 0x5635f39fbaf8>
 #> 
 
 as.list(method, args = c("id", "time"))
@@ -185,7 +185,7 @@ 
Examples

 #> [1] "Time"
 #> 
 
-if (require("kml")) {
+if (require("kml")) {
   method <- lcMethodKML("Y", id = "Id", time = "Time")
   as.list(method)
 
@@ -229,14 +229,14 @@ 
Examples

 #> function () 
 #> {
 #> }
-#> <environment: 0x55f64b76b918>
+#> <environment: 0x5635eb053978>
 #> 
 #> $centerMethod
 #> function (x) 
 #> {
 #>     mean(x, na.rm = TRUE)
 #> }
-#> <bytecode: 0x55f64f4204f0>
+#> <bytecode: 0x5635ef3b6080>
 #> <environment: namespace:longitudinalData>
 #> 
 #> $startingCond
@@ -262,15 +262,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/assert.html b/reference/assert.html
index 675de962..ff0b455c 100644
--- a/reference/assert.html
+++ b/reference/assert.html
@@ -12,7 +12,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
latrend-specific assertions

-    Source: R/assert.R
+    Source: R/assert.R
     
     

 
@@ -105,39 +105,41 @@ 
latrend-specific assertions

 
     

     
Arguments

-    
object

+
+
+
object

 
The object to test.

 
 
-
which

+
which

 
The argument names. Ellipsis (...) will be ignored.

 
 
-
pp

+
pp

 
The posterior probability matrix.

 
 
-
model

+
model

 
The lcModel object. Optional.

 
 
-
id

+
id

 
The id variable

 
 
-
ids

+
ids

 
Optional character vector of trajectory identifiers that are expected to be present in the data.

 
 
-
time

+
time

 
The time variable

 
 
-
response

+
response

 
The response column name. Optional.

 
 
-
min

+
min

 
The minimum required number.

 
 

@@ -154,15 +156,15 @@ 
Arguments

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/clusterNames-set.html b/reference/clusterNames-set.html
index 8f01c710..2c52cbf1 100644
--- a/reference/clusterNames-set.html
+++ b/reference/clusterNames-set.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Update the cluster names

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -71,19 +71,19 @@ 
Update the cluster names

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object to update.

 
 
-
value

+
value

 
The character with the new names.

 
 

     

     
Value

-    
-
-
The updated lcModel object.

+    
The updated lcModel object.

     

 
     

@@ -106,15 +106,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/clusterNames.html b/reference/clusterNames.html
index 52982e71..007e93cb 100644
--- a/reference/clusterNames.html
+++ b/reference/clusterNames.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get the cluster names

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -71,23 +71,23 @@ 
Get the cluster names

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
factor

+
factor

 
Whether to return the cluster names as a factor.

 
 

     

     
Value

-    
-
-
A character of the cluster names.

+    
A character of the cluster names.

     

     

     
See also

-    
Other lcModel functions: 
+    
Other lcModel functions:
 clusterProportions(),
 clusterSizes(),
 clusterTrajectories(),
@@ -145,15 +145,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/clusterProportions.html b/reference/clusterProportions.html
index c1c153d4..9f12b7f6 100644
--- a/reference/clusterProportions.html
+++ b/reference/clusterProportions.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Proportional size of each cluster

-    Source: R/generics.R, R/model.R
+    Source: R/generics.R, R/model.R
     
     

 
@@ -68,29 +68,29 @@ 
Proportional size of each cluster

     

     
clusterProportions(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 clusterProportions(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The model.

 
 
-
...

+
...

 
For lcModel objects: Additional arguments passed to postprob().

 
 

     

     
Value

-    
-
-
A named numeric vector of length nClusters(object) with the proportional size of each cluster.

+    
A named numeric vector of length nClusters(object) with the proportional size of each cluster.

     

     

     
lcModel

-    
+
 
 
By default, the cluster proportions are determined from the cluster-averaged posterior probabilities of the fitted data (as computed by the postprob() function).

 
Classes extending lcModel can override this method to return, for example, the exact estimated mixture proportions based on the model coefficients.

@@ -103,7 +103,7 @@ 
lcModel

     
See also

     
nClusters clusterNames

 
clusterSizes postprob

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterSizes(),
 clusterTrajectories(),
@@ -162,15 +162,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/clusterSizes.html b/reference/clusterSizes.html
index 44465f74..a53ca0a3 100644
--- a/reference/clusterSizes.html
+++ b/reference/clusterSizes.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Number of trajectories per cluster

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -71,19 +71,19 @@ 
Number of trajectories per cluster

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
...

+
...

 
Additional arguments passed to trajectoryAssignments().

 
 

     

     
Value

-    
-
-
A named integer vector of length nClusters(object) with the number of assigned trajectories per cluster.

+    
A named integer vector of length nClusters(object) with the number of assigned trajectories per cluster.

     

     

     
Details

@@ -92,7 +92,7 @@ 
Details

     

     
See also

     
clusterProportions trajectoryAssignments

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterTrajectories(),
@@ -151,15 +151,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/clusterTrajectories.html b/reference/clusterTrajectories.html
index d0fd6cda..9c5fef3a 100644
--- a/reference/clusterTrajectories.html
+++ b/reference/clusterTrajectories.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract cluster trajectories

-    Source: R/generics.R, R/model.R
+    Source: R/generics.R, R/model.R
     
     

 
@@ -68,33 +68,33 @@ 
Extract cluster trajectories

     

     
clusterTrajectories(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 clusterTrajectories(object, at = time(object), what = "mu", ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The model.

 
 
-
...

+
...

 
For lcModel objects: Arguments passed to predict.lcModel.

 
 
-
at

+
at

 
A numeric vector of the times at which to compute the cluster trajectories.

 
 
-
what

+
what

 
The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

 
 

     

     
Value

-    
-
-
A data.frame of the estimated values at the specified times.
+    
A data.frame of the estimated values at the specified times.
 The first column should be named "Cluster".
 The second column should be time, with the name matching the timeVariable(object).
 The third column should be the expected value of the observations, named after the responseVariable(object).

@@ -102,7 +102,7 @@ 
Value

     

     
See also

     
plotClusterTrajectories

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -189,15 +189,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/coef.lcModel.html b/reference/coef.lcModel.html
index 83019992..87ea7d78 100644
--- a/reference/coef.lcModel.html
+++ b/reference/coef.lcModel.html
@@ -5,7 +5,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract lcModel coefficients

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -70,29 +70,29 @@ 
Extract lcModel coefficients

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 coef(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
...

+
...

 
Additional arguments.

 
 

     

     
Value

-    
-
-
A named numeric vector with all coefficients, or a matrix with each column containing the cluster-specific coefficients. If coef() is not defined for the given model, an empty numeric vector is returned.

+    
A named numeric vector with all coefficients, or a matrix with each column containing the cluster-specific coefficients. If coef() is not defined for the given model, an empty numeric vector is returned.

     

     

     
Implementation

-    
+
 
 
Classes extending lcModel can override this method to return model-specific coefficients.

 coef.lcModelExt <- function(object, ...) {
@@ -102,7 +102,7 @@ 
Implementation

     

     

     
See also

-    
Other lcModel functions: 
+    
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -137,7 +137,7 @@ 
See also

 strip(),
 time.lcModel(),
 trajectoryAssignments()

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -197,15 +197,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/compose.html b/reference/compose.html
index f5314c79..33cefba2 100644
--- a/reference/compose.html
+++ b/reference/compose.html
@@ -6,7 +6,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
lcMethod estimation step: compose an lcMethod object

-    Source: R/generics.R, R/method.R
+    Source: R/generics.R, R/method.R
     
     

 
@@ -74,33 +74,33 @@ 
lcMethod estimation step: compose an lcMethod object

     

     
compose(method, envir, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 compose(method, envir = NULL)

     

 
     

     
Arguments

-    
method

+
+
+
method

 
The lcMethod object.

 
 
-
envir

+
envir

 
The environment in which the lcMethod should be evaluated

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
The evaluated and finalized lcMethod object.

+    
The evaluated and finalized lcMethod object.

     

     

     
Implementation

-    
+
 
 
In general, there is no need to extend this method for a specific method, as all arguments are automatically evaluated by the compose,lcMethod method.

 
However, in case there is a need to extend processing or to prevent evaluation of specific arguments (e.g., for handling errors), the method can be overridden for the specific lcMethod subclass.

@@ -113,7 +113,7 @@ 
Implementation

     

     

     
Estimation procedure

-    
+
 
 
The steps for estimating a lcMethod object are defined and executed as follows:
  1. compose(): Evaluate and finalize the method argument values.
    2. 
 
  2. validate(): Check the validity of the method argument values in relation to the dataset.
    3. 
@@ -140,15 +140,15 @@ 
    See also
    
 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/confusionMatrix.html b/reference/confusionMatrix.html
index 52722cfa..29f95017 100644
--- a/reference/confusionMatrix.html
+++ b/reference/confusionMatrix.html
@@ -5,7 +5,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Compute the posterior confusion matrix

-    Source: R/model-evaluation.R
+    Source: R/model-evaluation.R
     
     

 
@@ -75,28 +75,28 @@ 
Compute the posterior confusion matrix

 
     

     
Arguments

-    
object

+
+
+
object

 
The model, of type lcModel.

 
 
-
strategy

+
strategy

 
The strategy for assigning trajectories to a specific cluster, see trajectoryAssignments().
 If strategy = NULL, the posterior probabilities are used as weights (analogous to a repeated evaluation of strategy = which.weight).

 
 
-
scale

+
scale

 
Whether to express the confusion in probabilities (scale = TRUE), or in terms of the number of trajectories.

 
 
-
...

+
...

 
Additional arguments passed to trajectoryAssignments().

 
 

     

     
Value

-    
-
-
A K-by-K confusion matrix with K = nClusters(object).

+    
A K-by-K confusion matrix with K = nClusters(object).

     

     

     
See also

@@ -135,15 +135,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/converged.html b/reference/converged.html
index db0eefe5..b3726800 100644
--- a/reference/converged.html
+++ b/reference/converged.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Check model convergence

-    Source: R/generics.R, R/model.R
+    Source: R/generics.R, R/model.R
     
     

 
@@ -68,32 +68,30 @@ 
Check model convergence

     

     
converged(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 converged(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The model.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
Either logical indicating convergence, or a numeric status code.

-
-
+    
Either logical indicating convergence, or a numeric status code.

 
The default lcModel implementation returns NA.

     

     

     
Implementation

-    
+
 
 
Classes extending lcModel can override this method to return a convergence status or code.

 setMethod("converged", "lcModelExt", function(object, ...) {
@@ -103,7 +101,7 @@ 
Implementation

     

     

     
See also

-    
Other lcModel functions: 
+    
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -161,15 +159,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/createTestDataFold.html b/reference/createTestDataFold.html
index 8430227a..6d59afd6 100644
--- a/reference/createTestDataFold.html
+++ b/reference/createTestDataFold.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Create the test fold data for validation

-    Source: R/latrend.R
+    Source: R/latrend.R
     
     

 
@@ -71,22 +71,24 @@ 
Create the test fold data for validation

 
     

     
Arguments

-    
data

+
+
+
data

 
A data.frame representing the complete dataset.

 
 
-
trainData

+
trainData

 
A data.frame representing the training data, which should be a subset of data.

 
 
-
id

+
id

 
The trajectory identifier variable.

 
 

     

     
See also

     
createTrainDataFolds

-
Other validation methods: 
+
Other validation methods:
 createTestDataFolds(),
 createTrainDataFolds(),
 latrendBoot(),
@@ -123,15 +125,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/createTestDataFolds.html b/reference/createTestDataFolds.html
index 50eff9a5..e8a263e8 100644
--- a/reference/createTestDataFolds.html
+++ b/reference/createTestDataFolds.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Create all k test folds from the training data

-    Source: R/latrend.R
+    Source: R/latrend.R
     
     

 
@@ -71,21 +71,23 @@ 
Create all k test folds from the training data

 
     

     
Arguments

-    
data

+
+
+
data

 
A data.frame representing the complete dataset.

 
 
-
trainDataList

+
trainDataList

 
A list of data.frame representing each of the data training folds. These should be derived from data.

 
 
-
...

+
...

 
Arguments passed to createTestDataFold.

 
 

     

     
See also

-    
Other validation methods: 
+    
Other validation methods:
 createTestDataFold(),
 createTrainDataFolds(),
 latrendBoot(),
@@ -115,15 +117,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/createTrainDataFolds.html b/reference/createTrainDataFolds.html
index 8d6d1108..814d144d 100644
--- a/reference/createTrainDataFolds.html
+++ b/reference/createTrainDataFolds.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Create the training data for each of the k models in k-fold cross validation evaluation

-    Source: R/latrend.R
+    Source: R/latrend.R
     
     

 
@@ -76,31 +76,31 @@ 
Create the training data for each of the k models in k-fold cross validation
 
     

     
Arguments

-    
data

+
+
+
data

 
A data.frame representing the complete dataset.

 
 
-
folds

+
folds

 
The number of folds. By default, a 10-fold scheme is used.

 
 
-
id

+
id

 
The trajectory identifier variable.

 
 
-
seed

+
seed

 
The seed to use, in order to ensure reproducible fold generation at a later moment.

 
 

     

     
Value

-    
-
-
A list of data.frame of the folds training datasets.

+    
A list of data.frame of the folds training datasets.

     

     

     
See also

-    
Other validation methods: 
+    
Other validation methods:
 createTestDataFold(),
 createTestDataFolds(),
 latrendBoot(),
@@ -133,15 +133,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/defineExternalMetric.html b/reference/defineExternalMetric.html
index 13c54ac5..3229a630 100644
--- a/reference/defineExternalMetric.html
+++ b/reference/defineExternalMetric.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Define an external metric for lcModels

-    Source: R/metricsExternal.R
+    Source: R/metricsExternal.R
     
     

 
@@ -75,21 +75,23 @@ 
Define an external metric for lcModels

 
     

     
Arguments

-    
name

+
+
+
name

 
The name of the metric.

 
 
-
fun

+
fun

 
The function to compute the metric, accepting a lcModel object as input.

 
 
-
warnIfExists

+
warnIfExists

 
Whether to output a warning when the metric is already defined.

 
 

     

     
See also

-    
Other metric functions: 
+    
Other metric functions:
 defineInternalMetric(),
 externalMetric(),
 getExternalMetricDefinition(),
@@ -111,15 +113,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/defineInternalMetric.html b/reference/defineInternalMetric.html
index e79b323b..70a0c5e7 100644
--- a/reference/defineInternalMetric.html
+++ b/reference/defineInternalMetric.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Define an internal metric for lcModels

-    Source: R/metricsInternal.R
+    Source: R/metricsInternal.R
     
     

 
@@ -75,21 +75,23 @@ 
Define an internal metric for lcModels

 
     

     
Arguments

-    
name

+
+
+
name

 
The name of the metric.

 
 
-
fun

+
fun

 
The function to compute the metric, accepting a lcModel object as input.

 
 
-
warnIfExists

+
warnIfExists

 
Whether to output a warning when the metric is already defined.

 
 

     

     
See also

-    
Other metric functions: 
+    
Other metric functions:
 defineExternalMetric(),
 externalMetric(),
 getExternalMetricDefinition(),
@@ -123,15 +125,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/deviance.lcModel.html b/reference/deviance.lcModel.html
index 19240418..f2491cf2 100644
--- a/reference/deviance.lcModel.html
+++ b/reference/deviance.lcModel.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
lcModel deviance

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -66,25 +66,25 @@ 
lcModel deviance

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 deviance(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
...

+
...

 
Additional arguments.

 
 

     

     
Value

-    
-
-
A numeric with the deviance value. If unavailable, NA is returned.

+    
A numeric with the deviance value. If unavailable, NA is returned.

     

     

     
Details

@@ -93,7 +93,7 @@ 
Details

     

     
See also

     
stats::deviance metric

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -142,15 +142,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/df.residual.lcModel.html b/reference/df.residual.lcModel.html
index ca883769..8b867a8a 100644
--- a/reference/df.residual.lcModel.html
+++ b/reference/df.residual.lcModel.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract the residual degrees of freedom from a lcModel

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -66,30 +66,30 @@ 
Extract the residual degrees of freedom from a lcModel

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 df.residual(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
...

+
...

 
Additional arguments.

 
 

     

     
Value

-    
-
-
A numeric with the residual degrees of freedom. If unavailable, NA is returned.

+    
A numeric with the residual degrees of freedom. If unavailable, NA is returned.

     

     

     
See also

     
stats::df.residual nobs residuals

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -138,15 +138,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/dot-defineInternalDistanceMetrics.html b/reference/dot-defineInternalDistanceMetrics.html
index 29b92441..51cdf537 100644
--- a/reference/dot-defineInternalDistanceMetrics.html
+++ b/reference/dot-defineInternalDistanceMetrics.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Define the distance metrics for multiple types at once

-    Source: R/metricsInternal.R
+    Source: R/metricsInternal.R
     
     

 
@@ -91,15 +91,15 @@ 
Define the distance metrics for multiple types at once

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/dot-guessResponseVariable.html b/reference/dot-guessResponseVariable.html
index aa40287c..4e547cf8 100644
--- a/reference/dot-guessResponseVariable.html
+++ b/reference/dot-guessResponseVariable.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Guess the response variable

-    Source: R/variables.R
+    Source: R/variables.R
     
     

 
@@ -76,19 +76,21 @@ 
Guess the response variable

 
     

     
Arguments

-    
data

+
+
+
data

 
A trajectories data.frame.

 
 
-
id

+
id

 
Id variable(s) to exclude.

 
 
-
time

+
time

 
Time variable(s) to exclude.

 
 
-
cluster

+
cluster

 
Cluster variable(s) to exclude.

 
 

@@ -105,15 +107,15 @@ 
Arguments

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/dot-trajSubset.html b/reference/dot-trajSubset.html
index 7c207711..b1584f87 100644
--- a/reference/dot-trajSubset.html
+++ b/reference/dot-trajSubset.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Select trajectories

-    Source: R/latrend.R
+    Source: R/latrend.R
     
     

 
@@ -71,15 +71,17 @@ 
Select trajectories

 
     

     
Arguments

-    
data

+
+
+
data

 
The longitudinal dataset, a data.frame

 
 
-
ids

+
ids

 
The trajectory identifiers, vector

 
 
-
id

+
id

 
The name of the id column

 
 

@@ -96,15 +98,15 @@ 
Arguments

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/estimationTime.html b/reference/estimationTime.html
index 8cb834f8..43a2cf87 100644
--- a/reference/estimationTime.html
+++ b/reference/estimationTime.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Estimation time

-    Source: R/generics.R, R/model.R, R/models.R
+    Source: R/generics.R, R/model.R, R/models.R
     
     

 
@@ -70,41 +70,41 @@ 
Estimation time

     

     
estimationTime(object, unit = "secs", ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 estimationTime(object, unit = "secs", ...)
 
-# S4 method for lcModels
+# S4 method for class 'lcModels'
 estimationTime(object, unit = "secs", ...)
 
-# S4 method for list
+# S4 method for class 'list'
 estimationTime(object, unit = "secs", ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The model.

 
 
-
unit

+
unit

 
The time unit in which the estimation time should be outputted.
 By default, estimation time is in seconds.
 For accepted units, see base::difftime.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A non-negative scalar numeric representing the estimation time in the specified unit..

+    
A non-negative scalar numeric representing the estimation time in the specified unit..

     

     

     
See also

-    
Other lcModel functions: 
+    
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -148,11 +148,11 @@ 
Examples

 model <- latrend(method, latrendData)
 
 estimationTime(model)
-#> [1] 0.01
+#> [1] 0.008
 estimationTime(model, unit = 'mins')
-#> [1] 0.0001666667
+#> [1] 0.0001333333
 estimationTime(model, unit = 'days')
-#> [1] 1.157407e-07
+#> [1] 9.259259e-08
@@ -167,15 +167,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/evaluate.lcMethod.html b/reference/evaluate.lcMethod.html index 47c4d3cb..1af28723 100644 --- a/reference/evaluate.lcMethod.html +++ b/reference/evaluate.lcMethod.html @@ -3,7 +3,7 @@ - +
- +

Substitute the call arguments for their evaluated values

- Source: R/method.R + Source: R/method.R
@@ -66,7 +66,7 @@

Substitute the call arguments for their evaluated values

- 
# S3 method for lcMethod
+    
# S3 method for class 'lcMethod'
 evaluate(
   object,
   classes = "ANY",
@@ -79,40 +79,40 @@ 
Substitute the call arguments for their evaluated values

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcMethod object.

 
 
-
classes

+
classes

 
Substitute only arguments with specific class types. By default, all types are substituted.

 
 
-
try

+
try

 
Whether to try to evaluate arguments and ignore errors (the default), or to fail on any argument evaluation error.

 
 
-
exclude

+
exclude

 
Arguments to exclude from evaluation.

 
 
-
envir

+
envir

 
The environment in which to evaluate the arguments. If NULL, the environment associated with the object is used. If not available, the parent.frame() is used.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A new lcMethod object with the substituted arguments.

+    
A new lcMethod object with the substituted arguments.

     

     

     
See also

     
compose

-
Other lcMethod functions: 
+
Other lcMethod functions:
 [[,lcMethod-method,
 as.data.frame.lcMethod(),
 as.data.frame.lcMethods(),
@@ -136,15 +136,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/externalMetric.html b/reference/externalMetric.html
index e8fbf5f9..2f2f21aa 100644
--- a/reference/externalMetric.html
+++ b/reference/externalMetric.html
@@ -3,10 +3,10 @@
 Note that there are many external metrics available, and there exists no external metric that works best in all scenarios.
 It is recommended to carefully consider which metric is most appropriate for your use case.
 Many of the external metrics depend on implementations in other packages:
-clusterCrit  (Desgraupes 2018)
-mclustcomp  (You 2018)
-igraph  (Csardi and Nepusz 2006)
-psych  (Revelle 2019)
+clusterCrit  desgraupes2018clustercritlatrend
+mclustcomp  you2018mclustcomplatrend
+igraph  csardi2006igraphlatrend
+psych  revelle2019psychlatrend
 
 
 See mclustcomp::mclustcomp() for a grouped overview of similarity metrics.
@@ -15,7 +15,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Compute external model metric(s)

-    Source: R/generics.R, R/model.R, R/metricsExternal.R, and 1 more
+    Source: R/generics.R, R/model.R, R/metricsExternal.R, and 1 more
     
     

 
@@ -77,17 +77,17 @@ 
Compute external model metric(s)

     
Compute one or more external metrics for two or more objects.

 
Note that there are many external metrics available, and there exists no external metric that works best in all scenarios.
 It is recommended to carefully consider which metric is most appropriate for your use case.

-
Many of the external metrics depend on implementations in other packages:
  • clusterCrit  (Desgraupes 2018)
    • 
-
  • mclustcomp  (You 2018)
    • 
-
  • igraph  (Csardi and Nepusz 2006)
    • 
-
  • psych  (Revelle 2019)
    • 
+
    Many of the external metrics depend on implementations in other packages:
    • clusterCrit  desgraupes2018clustercritlatrend
      • 
+
    • mclustcomp  you2018mclustcomplatrend
      • 
+
    • igraph  csardi2006igraphlatrend
      • 
+
    • psych  revelle2019psychlatrend
      • 
 
    See mclustcomp::mclustcomp() for a grouped overview of similarity metrics.
    
 
    Call getInternalMetricNames() to retrieve the names of the defined internal metrics.
 Call getExternalMetricNames() to retrieve the names of the defined internal metrics.
    
     

 
     

-    
# S4 method for lcModel,lcModel
+    
# S4 method for class 'lcModel,lcModel'
 externalMetric(
   object,
   object2,
@@ -95,106 +95,74 @@ 
Compute external model metric(s)

   ...
 )
 
-# S4 method for lcModels,missing
+# S4 method for class 'lcModels,missing'
 externalMetric(object, object2, name = "adjustedRand")
 
-# S4 method for lcModels,character
+# S4 method for class 'lcModels,character'
 externalMetric(object, object2 = "adjustedRand")
 
-# S4 method for lcModels,lcModel
+# S4 method for class 'lcModels,lcModel'
 externalMetric(object, object2, name, drop = TRUE)
 
-# S4 method for list,lcModel
+# S4 method for class 'list,lcModel'
 externalMetric(object, object2, name, drop = TRUE)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The object to compare to the second object

 
 
-
object2

+
object2

 
The second object

 
 
-
name

+
name

 
The name(s) of the external metric(s) to compute. If no names are given, the names specified in the latrend.externalMetric option (none by default) are used.

 
 
-
...

+
...

 
Additional arguments.

 
 
-
drop

+
drop

 
Whether to return a numeric vector instead of a data.frame
 in case of a single metric.

 
 

     

     
Value

-    
-
-
For externalMetric(lcModel, lcModel): A numeric vector of the computed metrics.

-
-
+    
For externalMetric(lcModel, lcModel): A numeric vector of the computed metrics.

 
For externalMetric(lcModels): A distance matrix of class dist representing
 the pairwise comparisons.

-
-
 
For externalMetric(lcModels, name): A distance matrix of class dist representing
 the pairwise comparisons.

-
-
-
For externalMetric(lcModels, lcModel): A named numeric vector or data.frame

-
-
-
containing the computed model metrics.

-
-
-
For externalMetric(list, lcModel): A named numeric vector or data.frame

-
-
-
containing the computed model metrics.

+
For externalMetric(lcModels, lcModel): A named numeric vector or data.frame
+containing the computed model metrics.

+
For externalMetric(list, lcModel): A named numeric vector or data.frame
+containing the computed model metrics.

     

     

     
Supported external metrics

-    
-
Metric nameDescriptionFunction / Reference
adjustedRandAdjusted Rand index. Based on the Rand index, but adjusted for agreements occurring by chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.mclustcomp::mclustcomp(), (Hubert and Arabie 1985)
CohensKappaCohen's kappa. A partitioning agreement metric correcting for random chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.psych::cohen.kappa(), (Cohen 1960)
FF-scoremclustcomp::mclustcomp()
F1F1-score, also referred to as the Sørensen–Dice Coefficient, or Dice similarity coefficientmclustcomp::mclustcomp()
FolkesMallowsFowlkes-Mallows indexmclustcomp::mclustcomp()
HubertHubert indexclusterCrit::extCriteria()
JaccardJaccard indexmclustcomp::mclustcomp()
jointEntropyJoint entropy between model assignmentsmclustcomp::mclustcomp()
KulczynskiKulczynski indexclusterCrit::extCriteria()
MaximumMatchMaximum match measuremclustcomp::mclustcomp()
McNemarMcNemar statisticclusterCrit::extCriteria()
MeilaHeckermanMeila-Heckerman measuremclustcomp::mclustcomp()
MirkinMirkin metricmclustcomp::mclustcomp()
MIMutual informationmclustcomp::mclustcomp()
NMINormalized mutual informationigraph::compare()
NSJNormalized version of splitJoin. The proportion of edits relative to the maximum changes (twice the number of ids)
NVINormalized variation of informationmclustcomp::mclustcomp()
OverlapOverlap coefficient, also referred to as the Szymkiewicz–Simpson coefficientmclustcomp::mclustcomp() (M K and K 2016)
PDPartition differencemclustcomp::mclustcomp()
PhiPhi coefficient.clusterCrit::extCriteria()
precisionprecisionclusterCrit::extCriteria()
RandRand indexmclustcomp::mclustcomp()
recallrecallclusterCrit::extCriteria()
RogersTanimotoRogers-Tanimoto dissimilarityclusterCrit::extCriteria()
RusselRaoRussell-Rao dissimilarityclusterCrit::extCriteria()
SMCSimple matching coefficientmclustcomp::mclustcomp()
splitJointotal split-join indexigraph::split_join_distance()
splitJoin.refSplit-join index of the first model to the second model. In other words, it is the edit-distance between the two partitionings.
SokalSneath1Type-1 Sokal-Sneath dissimilarityclusterCrit::extCriteria()
SokalSneath2Type-2 Sokal-Sneath dissimilarityclusterCrit::extCriteria()
VIVariation of informationmclustcomp::mclustcomp()
Wallace1Type-1 Wallace criterionmclustcomp::mclustcomp()
Wallace2Type-2 Wallace criterionmclustcomp::mclustcomp()
WMSSEWeighted minimum sum of squared errors between cluster trajectories
WMMSEWeighted minimum mean of squared errors between cluster trajectories
WMMAEWeighted minimum mean of absolute errors between cluster trajectories

+
+
Metric nameDescriptionFunction / Reference
adjustedRandAdjusted Rand index. Based on the Rand index, but adjusted for agreements occurring by chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.mclustcomp::mclustcomp(), hubert1985comparinglatrend
CohensKappaCohen's kappa. A partitioning agreement metric correcting for random chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.psych::cohen.kappa(), cohen1960coefficientlatrend
FF-scoremclustcomp::mclustcomp()
F1F1-score, also referred to as the Sørensen–Dice Coefficient, or Dice similarity coefficientmclustcomp::mclustcomp()
FolkesMallowsFowlkes-Mallows indexmclustcomp::mclustcomp()
HubertHubert indexclusterCrit::extCriteria()
JaccardJaccard indexmclustcomp::mclustcomp()
jointEntropyJoint entropy between model assignmentsmclustcomp::mclustcomp()
KulczynskiKulczynski indexclusterCrit::extCriteria()
MaximumMatchMaximum match measuremclustcomp::mclustcomp()
McNemarMcNemar statisticclusterCrit::extCriteria()
MeilaHeckermanMeila-Heckerman measuremclustcomp::mclustcomp()
MirkinMirkin metricmclustcomp::mclustcomp()
MIMutual informationmclustcomp::mclustcomp()
NMINormalized mutual informationigraph::compare()
NSJNormalized version of splitJoin. The proportion of edits relative to the maximum changes (twice the number of ids)
NVINormalized variation of informationmclustcomp::mclustcomp()
OverlapOverlap coefficient, also referred to as the Szymkiewicz–Simpson coefficientmclustcomp::mclustcomp() vijaymeena2016surveylatrend
PDPartition differencemclustcomp::mclustcomp()
PhiPhi coefficient.clusterCrit::extCriteria()
precisionprecisionclusterCrit::extCriteria()
RandRand indexmclustcomp::mclustcomp()
recallrecallclusterCrit::extCriteria()
RogersTanimotoRogers-Tanimoto dissimilarityclusterCrit::extCriteria()
RusselRaoRussell-Rao dissimilarityclusterCrit::extCriteria()
SMCSimple matching coefficientmclustcomp::mclustcomp()
splitJointotal split-join indexigraph::split_join_distance()
splitJoin.refSplit-join index of the first model to the second model. In other words, it is the edit-distance between the two partitionings.
SokalSneath1Type-1 Sokal-Sneath dissimilarityclusterCrit::extCriteria()
SokalSneath2Type-2 Sokal-Sneath dissimilarityclusterCrit::extCriteria()
VIVariation of informationmclustcomp::mclustcomp()
Wallace1Type-1 Wallace criterionmclustcomp::mclustcomp()
Wallace2Type-2 Wallace criterionmclustcomp::mclustcomp()
WMSSEWeighted minimum sum of squared errors between cluster trajectories
WMMSEWeighted minimum mean of squared errors between cluster trajectories
WMMAEWeighted minimum mean of absolute errors between cluster trajectories

     

     
Implementation

-    
+
 
 
See the documentation of the defineExternalMetric() function for details on how to define your own external metrics.

     

     

     
References

-    
Cohen J (1960).
-“A Coefficient of Agreement for Nominal Scales.”
-Educational and Psychological Measurement, 20(1), 37-46.

 Csardi G, Nepusz T (2006).
-“The igraph software package for complex network research.”
-InterJournal, Complex Systems, 1695.
-https://igraph.org.

 Desgraupes B (2018).
-clusterCrit: Clustering Indices.
-R package version 1.2.8, https://CRAN.R-project.org/package=clusterCrit.

 Hubert L, Arabie P (1985).
-“Comparing Partitions.”
-Journal of Classification, 2(1), 193--218.
-ISSN 1432-1343, doi:10.1007/BF01908075
-.

 M K V, K K (2016).
-“A Survey on Similarity Measures in Text Mining.”
-Machine Learning and Applications: An International Journal, 3, 19-28.
-doi:10.5121/mlaij.2016.3103
-.

 Revelle W (2019).
-psych: Procedures for Psychological, Psychometric, and Personality Research.
-Northwestern University, Evanston, Illinois.
-R package version 1.9.12, https://CRAN.R-project.org/package=psych.

 You K (2018).
-mclustcomp: Measures for Comparing Clusters.
-R package version 0.3.1, https://CRAN.R-project.org/package=mclustcomp.

+    

     

     

     
See also

     
metric

-
Other metric functions: 
+
Other metric functions:
 defineExternalMetric(),
 defineInternalMetric(),
 getExternalMetricDefinition(),
@@ -202,7 +170,7 @@ 
See also

 getInternalMetricDefinition(),
 getInternalMetricNames(),
 metric()

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -266,15 +234,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/fit.html b/reference/fit.html
index fa0516ed..e71337d6 100644
--- a/reference/fit.html
+++ b/reference/fit.html
@@ -5,7 +5,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
lcMethod estimation step: logic for fitting the method to the processed data

-    Source: R/generics.R, R/method.R
+    Source: R/generics.R, R/method.R
     
     

 
@@ -72,41 +72,41 @@ 
lcMethod estimation step: logic for fitting the method to the p
     

     
fit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 fit(method, data, envir, verbose)

     

 
     

     
Arguments

-    
method

+
+
+
method

 
An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

 
 
-
data

+
data

 
A data.frame representing the transformed training data.

 
 
-
envir

+
envir

 
The environment containing variables generated by prepareData() and preFit().

 
 
-
verbose

+
verbose

 
A R.utils::Verbose object indicating the level of verbosity.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
The fitted object, inheriting from lcModel.

+    
The fitted object, inheriting from lcModel.

     

     

     
Implementation

-    
+
 
 
This method should be implemented for all lcMethod subclasses.

 

@@ -126,7 +126,7 @@ 
Implementation

     

     

     
Estimation procedure

-    
+
 
 
The steps for estimating a lcMethod object are defined and executed as follows:
  1. compose(): Evaluate and finalize the method argument values.
    2. 
 
  2. validate(): Check the validity of the method argument values in relation to the dataset.
    3. 
@@ -149,15 +149,15 @@ 
    Estimation procedure
    
 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/fitted.lcModel.html b/reference/fitted.lcModel.html
index c27913a4..cba17a11 100644
--- a/reference/fitted.lcModel.html
+++ b/reference/fitted.lcModel.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract lcModel fitted values

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -68,33 +68,33 @@ 
Extract lcModel fitted values

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 fitted(object, ..., clusters = trajectoryAssignments(object))

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
...

+
...

 
Additional arguments.

 
 
-
clusters

+
clusters

 
Optional cluster assignments per id. If unspecified, a matrix is returned containing the cluster-specific predictions per column.

 
 

     

     
Value

-    
-
-
A numeric vector of the fitted values for the respective class, or a matrix of fitted values for each cluster.

+    
A numeric vector of the fitted values for the respective class, or a matrix of fitted values for each cluster.

     

     

     
Implementation

-    
+
 
 
Classes extending lcModel can override this method to adapt the computation of the predicted values for the training data.
 Note that the implementation of this function is only needed when predict() and predictForCluster() are not defined for the lcModel subclass.

@@ -106,7 +106,7 @@ 
Implementation

     

     
See also

     
fittedTrajectories plotFittedTrajectories stats::fitted predict.lcModel trajectoryAssignments transformFitted

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -497,15 +497,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/fittedTrajectories.html b/reference/fittedTrajectories.html
index 7a3848d3..02c3ec64 100644
--- a/reference/fittedTrajectories.html
+++ b/reference/fittedTrajectories.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract the fitted trajectories

-    Source: R/generics.R, R/model.R
+    Source: R/generics.R, R/model.R
     
     

 
@@ -68,7 +68,7 @@ 
Extract the fitted trajectories

     

     
fittedTrajectories(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 fittedTrajectories(
   object,
   at = time(object),
@@ -80,34 +80,32 @@ 
Extract the fitted trajectories

 
     

     
Arguments

-    
object

+
+
+
object

 
The model.

 
 
-
...

+
...

 
For lcModel: Additional arguments passed to fitted.lcModel.

 
 
-
at

+
at

 
The time points at which to compute the id-specific trajectories.
 The default implementation merely filters the output, i.e., fitted values can only be outputted for times at which the model was trained.

 
 
-
what

+
what

 
The distributional parameter to compute the response for.

 
 
-
clusters

+
clusters

 
The cluster assignments for the strata to base the trajectories on.

 
 

     

     
Value

-    
-
-
A data.frame representing the fitted response per trajectory per moment in time for the respective cluster.

-
-
+    
A data.frame representing the fitted response per trajectory per moment in time for the respective cluster.

 
For lcModel: A data.frame with columns id, time, response, and "Cluster".

     

     

@@ -117,7 +115,7 @@ 
Details

     

     
See also

     
plotFittedTrajectories

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -2580,15 +2578,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/formula.lcMethod.html b/reference/formula.lcMethod.html
index 2080b8d5..ac005f1e 100644
--- a/reference/formula.lcMethod.html
+++ b/reference/formula.lcMethod.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract formula

-    Source: R/method.R
+    Source: R/method.R
     
     

 
@@ -66,37 +66,37 @@ 
Extract formula

     

 
     

-    
# S3 method for lcMethod
+    
# S3 method for class 'lcMethod'
 formula(x, what = "mu", envir = NULL, ...)

     

 
     

     
Arguments

-    
x

+
+
+
x

 
The lcMethod object.

 
 
-
what

+
what

 
The distributional parameter to which this formula applies. By default, the formula specifies "mu".

 
 
-
envir

+
envir

 
The environment in which to evaluate the arguments. If NULL, the environment associated with the object is used. If not available, the parent.frame() is used.

 
 
-
...

+
...

 
Additional arguments.

 
 

     

     
Value

-    
-
-
The formula for the given distributional parameter.

+    
The formula for the given distributional parameter.

     

     

     
See also

-    
Other lcMethod functions: 
+    
Other lcMethod functions:
 [[,lcMethod-method,
 as.data.frame.lcMethod(),
 as.data.frame.lcMethods(),
@@ -113,7 +113,7 @@ 
Examples

     
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
 formula(method) # Y ~ Time
 #> Y ~ Time
-#> <environment: 0x55f6598f2f68>
+#> <environment: 0x5635fb4afd48>
 

     

   

@@ -128,15 +128,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/formula.lcModel.html b/reference/formula.lcModel.html
index d018f33f..c833beb5 100644
--- a/reference/formula.lcModel.html
+++ b/reference/formula.lcModel.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract the formula of a lcModel

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -68,29 +68,29 @@ 
Extract the formula of a lcModel

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 formula(x, what = "mu", ...)

     

 
     

     
Arguments

-    
x

+
+
+
x

 
The lcModel object.

 
 
-
what

+
what

 
The distributional parameter.

 
 
-
...

+
...

 
Additional arguments.

 
 

     

     
Value

-    
-
-
Returns the associated formula, or response ~ 0 if not specified.

+    
Returns the associated formula, or response ~ 0 if not specified.

     

     

     
See also

@@ -104,7 +104,7 @@ 
Examples

 model <- latrend(method, data = latrendData)
 formula(model) # Y ~ Time
 #> Y ~ Time
-#> <environment: 0x55f64e602f00>
+#> <environment: 0x5635ff952ea8>
 

     

   

@@ -119,15 +119,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/generateLongData-1.png b/reference/generateLongData-1.png
index 76430767..c9ee9b07 100644
Binary files a/reference/generateLongData-1.png and b/reference/generateLongData-1.png differ
diff --git a/reference/generateLongData.html b/reference/generateLongData.html
index 49df06ea..22d038d5 100644
--- a/reference/generateLongData.html
+++ b/reference/generateLongData.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Generate longitudinal test data

-    Source: R/data.R
+    Source: R/data.R
     
     

 
@@ -87,64 +87,66 @@ 
Generate longitudinal test data

 
     

     
Arguments

-    
sizes

+
+
+
sizes

 
Number of strata per cluster.

 
 
-
fixed

+
fixed

 
Fixed effects formula.

 
 
-
cluster

+
cluster

 
Cluster effects formula.

 
 
-
random

+
random

 
Random effects formula.

 
 
-
id

+
id

 
Name of the strata.

 
 
-
data

+
data

 
Data with covariates to use for generation. Stratified data may be specified by
 adding a grouping column.

 
 
-
fixedCoefs

+
fixedCoefs

 
Coefficients matrix for the fixed effects.

 
 
-
clusterCoefs

+
clusterCoefs

 
Coefficients matrix for the cluster effects.

 
 
-
randomScales

+
randomScales

 
Standard deviations matrix for the size of the variance components (random effects).

 
 
-
rrandom

+
rrandom

 
Random sampler for generating the variance components at location 0.

 
 
-
noiseScales

+
noiseScales

 
Scale of the random noise passed to rnoise. Either scalar or defined per cluster.

 
 
-
rnoise

+
rnoise

 
Random sampler for generating noise at location 0 with the respective scale.

 
 
-
clusterNames

+
clusterNames

 
A character vector denoting the names of the generated clusters.

 
 
-
shuffle

+
shuffle

 
Whether to randomly reorder the strata in which they appear in the data.frame.

 
 
-
seed

+
seed

 
Optional seed to set for the PRNG. The set PRNG state persists after the function completes.

 
 

@@ -179,15 +181,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getArgumentDefaults.html b/reference/getArgumentDefaults.html
index 47c9cc45..2b9a83ad 100644
--- a/reference/getArgumentDefaults.html
+++ b/reference/getArgumentDefaults.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Default argument values for the given method specification

-    Source: R/generics.R, R/method.R
+    Source: R/generics.R, R/method.R
     
     

 
@@ -70,29 +70,29 @@ 
Default argument values for the given method specification

     

     
getArgumentDefaults(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 getArgumentDefaults(object)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The method specification object.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A named list of argument values.

+    
A named list of argument values.

     

     

     
Implementation

-    
+
 
 
Although implementing this method is optional, it prevents users from
 having to specify all arguments every time they want to create a method specification.

@@ -116,7 +116,7 @@ 
Implementation

     
See also

     
getArgumentExclusions

 
lcMethod

-
Other lcMethod implementations: 
+
Other lcMethod implementations:
 getArgumentExclusions(),
 lcMethod-class,
 lcMethodAkmedoids,
@@ -150,15 +150,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getArgumentExclusions.html b/reference/getArgumentExclusions.html
index 81995a16..540bb6d7 100644
--- a/reference/getArgumentExclusions.html
+++ b/reference/getArgumentExclusions.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Arguments to be excluded from the specification

-    Source: R/generics.R, R/method.R
+    Source: R/generics.R, R/method.R
     
     

 
@@ -68,29 +68,29 @@ 
Arguments to be excluded from the specification

     

     
getArgumentExclusions(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 getArgumentExclusions(object)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The object.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A character vector of argument names.

+    
A character vector of argument names.

     

     

     
Implementation

-    
+
 
 
This function only needs to be implemented if you want to avoid users from specifying
 redundant arguments or arguments that are set automatically or conditionally on other arguments.

@@ -111,7 +111,7 @@ 
Implementation

     
See also

     
getArgumentDefaults

 
lcMethod getArgumentExclusions

-
Other lcMethod implementations: 
+
Other lcMethod implementations:
 getArgumentDefaults(),
 lcMethod-class,
 lcMethodAkmedoids,
@@ -145,15 +145,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getCall.lcModel.html b/reference/getCall.lcModel.html
index 1e1b3134..847ddeae 100644
--- a/reference/getCall.lcModel.html
+++ b/reference/getCall.lcModel.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get the model call

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -66,30 +66,30 @@ 
Get the model call

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 getCall(x, ...)

     

 
     

     
Arguments

-    
x

+
+
+
x

 
The lcModel object.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A call to latrend() with the necessary arguments and data.

+    
A call to latrend() with the necessary arguments and data.

     

     

     
See also

     
stats::getCall getLcMethod

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -151,15 +151,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getCitation.html b/reference/getCitation.html
index 95f59d25..17dde717 100644
--- a/reference/getCitation.html
+++ b/reference/getCitation.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get citation info

-    Source: R/generics.R, R/method.R, R/model.R
+    Source: R/generics.R, R/method.R, R/model.R
     
     

 
@@ -68,28 +68,28 @@ 
Get citation info

     

     
getCitation(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 getCitation(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 getCitation(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The object

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A utils::citation object.

+    
A utils::citation object.

     

     

     
See also

@@ -108,15 +108,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getExternalMetricDefinition.html b/reference/getExternalMetricDefinition.html
index 100fdd28..28abf539 100644
--- a/reference/getExternalMetricDefinition.html
+++ b/reference/getExternalMetricDefinition.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get the external metric definition

-    Source: R/metricsExternal.R
+    Source: R/metricsExternal.R
     
     

 
@@ -71,19 +71,19 @@ 
Get the external metric definition

 
     

     
Arguments

-    
name

+
+
+
name

 
The name of the metric.

 
 

     

     
Value

-    
-
-
The metric function, or NULL if not defined.

+    
The metric function, or NULL if not defined.

     

     

     
See also

-    
Other metric functions: 
+    
Other metric functions:
 defineExternalMetric(),
 defineInternalMetric(),
 externalMetric(),
@@ -105,15 +105,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getExternalMetricNames.html b/reference/getExternalMetricNames.html
index 30b4d641..0eb5917e 100644
--- a/reference/getExternalMetricNames.html
+++ b/reference/getExternalMetricNames.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get the names of the available external metrics

-    Source: R/metricsExternal.R
+    Source: R/metricsExternal.R
     
     

 
@@ -71,7 +71,7 @@ 
Get the names of the available external metrics

 
     

     
See also

-    
Other metric functions: 
+    
Other metric functions:
 defineExternalMetric(),
 defineInternalMetric(),
 externalMetric(),
@@ -93,15 +93,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getInternalMetricDefinition.html b/reference/getInternalMetricDefinition.html
index 3ed1c4a9..3be0953e 100644
--- a/reference/getInternalMetricDefinition.html
+++ b/reference/getInternalMetricDefinition.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get the internal metric definition

-    Source: R/metricsInternal.R
+    Source: R/metricsInternal.R
     
     

 
@@ -71,19 +71,19 @@ 
Get the internal metric definition

 
     

     
Arguments

-    
name

+
+
+
name

 
The name of the metric.

 
 

     

     
Value

-    
-
-
The metric function, or NULL if not defined.

+    
The metric function, or NULL if not defined.

     

     

     
See also

-    
Other metric functions: 
+    
Other metric functions:
 defineExternalMetric(),
 defineInternalMetric(),
 externalMetric(),
@@ -105,15 +105,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getInternalMetricNames.html b/reference/getInternalMetricNames.html
index ee137d72..4bd48d27 100644
--- a/reference/getInternalMetricNames.html
+++ b/reference/getInternalMetricNames.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get the names of the available internal metrics

-    Source: R/metricsInternal.R
+    Source: R/metricsInternal.R
     
     

 
@@ -71,7 +71,7 @@ 
Get the names of the available internal metrics

 
     

     
See also

-    
Other metric functions: 
+    
Other metric functions:
 defineExternalMetric(),
 defineInternalMetric(),
 externalMetric(),
@@ -93,15 +93,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getLabel.html b/reference/getLabel.html
index 6cb2fd6e..37fa7c0a 100644
--- a/reference/getLabel.html
+++ b/reference/getLabel.html
@@ -6,7 +6,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Object label

-    Source: R/generics.R, R/method.R, R/model.R
+    Source: R/generics.R, R/method.R, R/model.R
     
     

 
@@ -74,28 +74,28 @@ 
Object label

     

     
getLabel(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 getLabel(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 getLabel(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The object.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A scalar character. The empty string is returned if there is no label.

+    
A scalar character. The empty string is returned if there is no label.

     

     

     
See also

@@ -125,15 +125,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getLcMethod.html b/reference/getLcMethod.html
index 0c23a01d..752561cb 100644
--- a/reference/getLcMethod.html
+++ b/reference/getLcMethod.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get the method specification

-    Source: R/generics.R, R/model.R
+    Source: R/generics.R, R/model.R
     
     

 
@@ -68,30 +68,30 @@ 
Get the method specification

     

     
getLcMethod(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 getLcMethod(object)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The model.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
An lcMethod object.

+    
An lcMethod object.

     

     

     
See also

     
getCall.lcModel

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -155,15 +155,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/getName.html b/reference/getName.html
index b08fa2cd..473439fe 100644
--- a/reference/getName.html
+++ b/reference/getName.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Object name

-    Source: R/generics.R, R/method.R, R/model.R
+    Source: R/generics.R, R/method.R, R/model.R
     
     

 
@@ -72,40 +72,40 @@ 
Object name

 
 getShortName(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 getName(object, ...)
 
-# S4 method for NULL
+# S4 method for class 'NULL'
 getName(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 getShortName(object, ...)
 
-# S4 method for NULL
+# S4 method for class 'NULL'
 getShortName(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 getName(object)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 getShortName(object)

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Not used.

Value

- - -

A nonempty string, as character.

+

A nonempty string, as character.

Details

@@ -113,7 +113,7 @@

Details

Implementation

- +

When implementing your own lcMethod subclass, override these methods to provide full and abbreviated names.


 setMethod("getName", "lcMethodExample", function(object) "example name")
@@ -150,15 +150,15 @@ 
Examples
-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/idVariable.html b/reference/idVariable.html index 55666c2d..5a6f808a 100644 --- a/reference/idVariable.html +++ b/reference/idVariable.html @@ -3,7 +3,7 @@ - +
- +

Extract the trajectory identifier variable

- Source: R/generics.R, R/method.R, R/model.R, and 1 more + Source: R/generics.R, R/method.R, R/model.R, and 1 more
@@ -68,35 +68,35 @@

Extract the trajectory identifier variable

idVariable(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 idVariable(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 idVariable(object)
 
-# S4 method for ANY
+# S4 method for class 'ANY'
 idVariable(object)

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Not used.

Value

- - -

A nonempty string, as character.

+

A nonempty string, as character.

See also

-

Other variables: +

Other variables: responseVariable(), timeVariable()

@@ -125,15 +125,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/ids.html b/reference/ids.html index 41d13d25..43aad224 100644 --- a/reference/ids.html +++ b/reference/ids.html @@ -3,7 +3,7 @@ - +
- +

Get the trajectory ids on which the model was fitted

- Source: R/model.R + Source: R/model.R
@@ -71,15 +71,15 @@

Get the trajectory ids on which the model was fitted

Arguments

-
object
+ + +
object

The lcModel object.

Value

- - -

A character vector or integer vector of the identifier for every fitted trajectory.

+

A character vector or integer vector of the identifier for every fitted trajectory.

Details

@@ -87,7 +87,7 @@

Details

See also

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -156,15 +156,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/index.html b/reference/index.html index 14a412d1..1601fb0f 100644 --- a/reference/index.html +++ b/reference/index.html @@ -1,9 +1,9 @@ -Function reference • latrendPackage index • latrend - +
- +
@@ -644,15 +644,15 @@

All functions
-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/indexy.html b/reference/indexy.html index e583a610..84a3b868 100644 --- a/reference/indexy.html +++ b/reference/indexy.html @@ -3,7 +3,7 @@ - +
- +

Retrieve and evaluate a lcMethod argument by name

- Source: R/method.R + Source: R/method.R
@@ -66,44 +66,44 @@

Retrieve and evaluate a lcMethod argument by name

- 
# S4 method for lcMethod
-$(x, name)
-
-# S4 method for lcMethod
-[[(x, i, eval = TRUE, envir = NULL)
+ 
# S4 method for class 'lcMethod'
+x$name
+
+# S4 method for class 'lcMethod'
+x[[i, eval = TRUE, envir = NULL]]

Arguments

-
x
+ + +
x

The lcMethod object.

-
name
+
name

The argument name, as character.

-
i
+
i

Name or index of the argument to retrieve.

-
eval
+
eval

Whether to evaluate the call argument (enabled by default).

-
envir
+
envir

The environment in which to evaluate the argument. This argument is only applicable when eval = TRUE.

Value

- - -

The argument call or evaluation result.

+

The argument call or evaluation result.

See also

-

Other lcMethod functions: +

Other lcMethod functions: as.data.frame.lcMethod(), as.data.frame.lcMethods(), as.lcMethods(), @@ -142,15 +142,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/initialize-lcMethod-method.html b/reference/initialize-lcMethod-method.html index fde8e721..6c561b17 100644 --- a/reference/initialize-lcMethod-method.html +++ b/reference/initialize-lcMethod-method.html @@ -3,7 +3,7 @@ - +
- +

lcMethod initialization

- Source: R/method.R + Source: R/method.R
@@ -66,17 +66,19 @@

lcMethod initialization

- 
# S4 method for lcMethod
+    
# S4 method for class 'lcMethod'
 initialize(.Object, ...)

Arguments

-
.Object
+ + +
.Object

The newly allocated lcMethod object.

-
...
+
...

Other method arguments.

@@ -114,15 +116,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-akmedoids.html b/reference/interface-akmedoids.html index b1549e5c..4dc6af3e 100644 --- a/reference/interface-akmedoids.html +++ b/reference/interface-akmedoids.html @@ -3,7 +3,7 @@ - +
- +

akmedoids interface

- Source: R/methodAKMedoids.R + Source: R/methodAKMedoids.R
@@ -66,44 +66,46 @@

akmedoids interface

- 
# S4 method for lcMethodAkmedoids
+    
# S4 method for class 'lcMethodAkmedoids'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodAkmedoids
+# S4 method for class 'lcMethodAkmedoids'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodAkmedoids
+# S4 method for class 'lcMethodAkmedoids'
 getCitation(object, ...)
 
-# S4 method for lcMethodAkmedoids
+# S4 method for class 'lcMethodAkmedoids'
 getName(object)
 
-# S4 method for lcMethodAkmedoids
+# S4 method for class 'lcMethodAkmedoids'
 getShortName(object)
 
-# S4 method for lcMethodAkmedoids
+# S4 method for class 'lcMethodAkmedoids'
 fit(method, data, envir, verbose, ...)

Arguments

-
...
+ + +
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

@@ -124,15 +126,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-crimCV.html b/reference/interface-crimCV.html index a2880f39..82cb19a3 100644 --- a/reference/interface-crimCV.html +++ b/reference/interface-crimCV.html @@ -3,7 +3,7 @@ - +
- +

crimCV interface

- Source: R/methodCrimCV.R, R/modelCrimCV.R + Source: R/methodCrimCV.R, R/modelCrimCV.R
@@ -66,78 +66,80 @@

crimCV interface

- 
# S4 method for lcMethodCrimCV
+    
# S4 method for class 'lcMethodCrimCV'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodCrimCV
+# S4 method for class 'lcMethodCrimCV'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodCrimCV
+# S4 method for class 'lcMethodCrimCV'
 getCitation(object, ...)
 
-# S4 method for lcMethodCrimCV
+# S4 method for class 'lcMethodCrimCV'
 getName(object)
 
-# S4 method for lcMethodCrimCV
+# S4 method for class 'lcMethodCrimCV'
 getShortName(object)
 
-# S4 method for lcMethodCrimCV
+# S4 method for class 'lcMethodCrimCV'
 prepareData(method, data, verbose, ...)
 
-# S4 method for lcMethodCrimCV
+# S4 method for class 'lcMethodCrimCV'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcModelCrimCV
+# S4 method for class 'lcModelCrimCV'
 predictForCluster(object, newdata, cluster, what = "mu", ...)
 
-# S4 method for lcModelCrimCV
+# S4 method for class 'lcModelCrimCV'
 postprob(object)
 
-# S3 method for lcModelCrimCV
+# S3 method for class 'lcModelCrimCV'
 logLik(object, ...)
 
-# S3 method for lcModelCrimCV
+# S3 method for class 'lcModelCrimCV'
 coef(object, ...)
 
-# S4 method for lcModelCrimCV
+# S4 method for class 'lcModelCrimCV'
 converged(object)

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

-
cluster
+
cluster

The cluster name (as character) to predict for.

-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

@@ -158,15 +160,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-custom.html b/reference/interface-custom.html index 26edc7ab..305ea036 100644 --- a/reference/interface-custom.html +++ b/reference/interface-custom.html @@ -3,7 +3,7 @@ - +
- +

function interface

- Source: R/modelPartition.R, R/methodFunction.R, R/methodRandom.R, and 2 more + Source: R/modelPartition.R, R/methodFunction.R, R/methodRandom.R, and 2 more
@@ -66,7 +66,7 @@

function interface

- 
# S4 method for lcModelPartition
+    
# S4 method for class 'lcModelPartition'
 clusterTrajectories(
   object,
   at = time(object),
@@ -75,52 +75,52 @@ 
function interface

   ...
 )
 
-# S4 method for lcModelPartition
+# S4 method for class 'lcModelPartition'
 converged(object, ...)
 
-# S4 method for lcModelPartition
+# S4 method for class 'lcModelPartition'
 getName(object, ...)
 
-# S4 method for lcModelPartition
+# S4 method for class 'lcModelPartition'
 getShortName(object, ...)
 
-# S4 method for lcModelPartition
+# S4 method for class 'lcModelPartition'
 postprob(object, ...)
 
-# S4 method for lcMethodFunction
+# S4 method for class 'lcMethodFunction'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodFunction
+# S4 method for class 'lcMethodFunction'
 getName(object)
 
-# S4 method for lcMethodFunction
+# S4 method for class 'lcMethodFunction'
 getShortName(object)
 
-# S4 method for lcMethodFunction
+# S4 method for class 'lcMethodFunction'
 prepareData(method, data, verbose)
 
-# S4 method for lcMethodFunction
+# S4 method for class 'lcMethodFunction'
 fit(method, data, envir, verbose)
 
-# S4 method for lcMethodRandom
+# S4 method for class 'lcMethodRandom'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodRandom
+# S4 method for class 'lcMethodRandom'
 getName(object)
 
-# S4 method for lcMethodRandom
+# S4 method for class 'lcMethodRandom'
 getShortName(object)
 
-# S4 method for lcMethodRandom
+# S4 method for class 'lcMethodRandom'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcModelStratify
+# S4 method for class 'lcModelStratify'
 converged(object, ...)
 
-# S4 method for lcModelStratify
+# S4 method for class 'lcModelStratify'
 predictPostprob(object, newdata = NULL, ...)
 
-# S4 method for lcModelWeightedPartition
+# S4 method for class 'lcModelWeightedPartition'
 clusterTrajectories(
   object,
   at = time(object),
@@ -129,50 +129,52 @@ 
function interface

   ...
 )
 
-# S4 method for lcModelWeightedPartition
+# S4 method for class 'lcModelWeightedPartition'
 converged(object, ...)
 
-# S4 method for lcModelWeightedPartition
+# S4 method for class 'lcModelWeightedPartition'
 getName(object, ...)
 
-# S4 method for lcModelWeightedPartition
+# S4 method for class 'lcModelWeightedPartition'
 getShortName(object, ...)
 
-# S4 method for lcModelWeightedPartition
+# S4 method for class 'lcModelWeightedPartition'
 postprob(object, ...)

Arguments

-
object
+ + +
object

The object.

-
center
+
center

The function to use to compute the cluster trajectory center at the respective moment in time.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
newdata
+
newdata

Optional data.frame for which to compute the posterior probability. If omitted, the model training data is used.

@@ -193,15 +195,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-dtwclust.html b/reference/interface-dtwclust.html index 662cca73..57a8c1c9 100644 --- a/reference/interface-dtwclust.html +++ b/reference/interface-dtwclust.html @@ -3,7 +3,7 @@ - +
- +

dtwclust interface

- Source: R/methodDtwclust.R, R/modelDtwclust.R + Source: R/methodDtwclust.R, R/modelDtwclust.R
@@ -66,72 +66,74 @@

dtwclust interface

- 
# S4 method for lcMethodDtwclust
+    
# S4 method for class 'lcMethodDtwclust'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodDtwclust
+# S4 method for class 'lcMethodDtwclust'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodDtwclust
+# S4 method for class 'lcMethodDtwclust'
 getCitation(object, ...)
 
-# S4 method for lcMethodDtwclust
+# S4 method for class 'lcMethodDtwclust'
 getName(object)
 
-# S4 method for lcMethodDtwclust
+# S4 method for class 'lcMethodDtwclust'
 getShortName(object)
 
-# S4 method for lcMethodDtwclust
+# S4 method for class 'lcMethodDtwclust'
 preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodDtwclust
+# S4 method for class 'lcMethodDtwclust'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcModelDtwclust
+# S4 method for class 'lcModelDtwclust'
 converged(object, ...)
 
-# S4 method for lcModelDtwclust
+# S4 method for class 'lcModelDtwclust'
 postprob(object, ...)
 
-# S4 method for lcModelDtwclust
+# S4 method for class 'lcModelDtwclust'
 predictForCluster(object, newdata, cluster, what = "mu", ...)

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

-
cluster
+
cluster

The cluster name (as character) to predict for.

-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

@@ -152,15 +154,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-featureBased.html b/reference/interface-featureBased.html index 4a896ddb..75c4b7ba 100644 --- a/reference/interface-featureBased.html +++ b/reference/interface-featureBased.html @@ -3,7 +3,7 @@ - +
- +

featureBased interface

- Source: R/methodFeature.R, R/methodLMKM.R, R/methodGCKM.R, and 2 more + Source: R/methodFeature.R, R/methodLMKM.R, R/methodGCKM.R, and 2 more
@@ -66,112 +66,114 @@

featureBased interface

- 
# S4 method for lcMethodFeature
+    
# S4 method for class 'lcMethodFeature'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodFeature
+# S4 method for class 'lcMethodFeature'
 getName(object)
 
-# S4 method for lcMethodFeature
+# S4 method for class 'lcMethodFeature'
 getShortName(object)
 
-# S4 method for lcMethodFeature
+# S4 method for class 'lcMethodFeature'
 prepareData(method, data, verbose, ...)
 
-# S4 method for lcMethodFeature
+# S4 method for class 'lcMethodFeature'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodLMKM
+# S4 method for class 'lcMethodLMKM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodLMKM
+# S4 method for class 'lcMethodLMKM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodLMKM
+# S4 method for class 'lcMethodLMKM'
 getName(object)
 
-# S4 method for lcMethodLMKM
+# S4 method for class 'lcMethodLMKM'
 getShortName(object)
 
-# S4 method for lcMethodLMKM
+# S4 method for class 'lcMethodLMKM'
 prepareData(method, data, verbose)
 
-# S4 method for lcMethodLMKM
+# S4 method for class 'lcMethodLMKM'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodLMKM
+# S4 method for class 'lcMethodLMKM'
 validate(method, data, envir = NULL, ...)
 
-# S4 method for lcMethodGCKM
+# S4 method for class 'lcMethodGCKM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodGCKM
+# S4 method for class 'lcMethodGCKM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodGCKM
+# S4 method for class 'lcMethodGCKM'
 getName(object)
 
-# S4 method for lcMethodGCKM
+# S4 method for class 'lcMethodGCKM'
 getShortName(object)
 
-# S4 method for lcMethodGCKM
+# S4 method for class 'lcMethodGCKM'
 prepareData(method, data, verbose)
 
-# S4 method for lcMethodStratify
+# S4 method for class 'lcMethodStratify'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodStratify
+# S4 method for class 'lcMethodStratify'
 getName(object)
 
-# S4 method for lcMethodStratify
+# S4 method for class 'lcMethodStratify'
 getShortName(object)
 
-# S4 method for lcMethodStratify
+# S4 method for class 'lcMethodStratify'
 compose(method, envir = NULL, ...)
 
-# S4 method for lcMethodStratify
+# S4 method for class 'lcMethodStratify'
 fit(method, data, envir, verbose, ...)
 
-# S3 method for lcModelLMKM
+# S3 method for class 'lcModelLMKM'
 coef(object, ..., cluster = NULL)
 
-# S3 method for lcModelLMKM
+# S3 method for class 'lcModelLMKM'
 logLik(object, ...)
 
-# S4 method for lcModelLMKM
+# S4 method for class 'lcModelLMKM'
 converged(object, ...)
 
-# S4 method for lcModelLMKM
+# S4 method for class 'lcModelLMKM'
 postprob(object, ...)

Arguments

-
object
+ + +
object

The object.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
...
+
...

Not used.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
cluster
+
cluster

The cluster name.

@@ -192,15 +194,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-flexmix.html b/reference/interface-flexmix.html index 991897d8..b29f59fd 100644 --- a/reference/interface-flexmix.html +++ b/reference/interface-flexmix.html @@ -3,7 +3,7 @@ - +
- +

flexmix interface

- Source: R/methodFlexmix.R, R/methodFlexmixGBTM.R, R/modelFlexmix.R + Source: R/methodFlexmix.R, R/methodFlexmixGBTM.R, R/modelFlexmix.R
@@ -66,100 +66,102 @@

flexmix interface

- 
# S4 method for lcMethodFlexmix
+    
# S4 method for class 'lcMethodFlexmix'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodFlexmix
+# S4 method for class 'lcMethodFlexmix'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodFlexmix
+# S4 method for class 'lcMethodFlexmix'
 getCitation(object, ...)
 
-# S4 method for lcMethodFlexmix
+# S4 method for class 'lcMethodFlexmix'
 getName(object)
 
-# S4 method for lcMethodFlexmix
+# S4 method for class 'lcMethodFlexmix'
 getShortName(object)
 
-# S4 method for lcMethodFlexmix
+# S4 method for class 'lcMethodFlexmix'
 preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodFlexmix
+# S4 method for class 'lcMethodFlexmix'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodFlexmixGBTM
+# S4 method for class 'lcMethodFlexmixGBTM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodFlexmixGBTM
+# S4 method for class 'lcMethodFlexmixGBTM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodFlexmixGBTM
+# S4 method for class 'lcMethodFlexmixGBTM'
 getName(object)
 
-# S4 method for lcMethodFlexmixGBTM
+# S4 method for class 'lcMethodFlexmixGBTM'
 getShortName(object)
 
-# S4 method for lcMethodFlexmixGBTM
+# S4 method for class 'lcMethodFlexmixGBTM'
 preFit(method, data, envir, verbose)
 
-# S3 method for lcModelFlexmix
+# S3 method for class 'lcModelFlexmix'
 fitted(object, ..., clusters = trajectoryAssignments(object))
 
-# S4 method for lcModelFlexmix
+# S4 method for class 'lcModelFlexmix'
 predictForCluster(object, newdata, cluster, what = "mu", ...)
 
-# S4 method for lcModelFlexmix
+# S4 method for class 'lcModelFlexmix'
 postprob(object, ...)
 
-# S3 method for lcModelFlexmix
+# S3 method for class 'lcModelFlexmix'
 logLik(object, ...)
 
-# S3 method for lcModelFlexmix
+# S3 method for class 'lcModelFlexmix'
 coef(object, ...)
 
-# S4 method for lcModelFlexmix
+# S4 method for class 'lcModelFlexmix'
 converged(object, ...)

Arguments

-
object
+ + +
object

The lcModel object.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
clusters
+
clusters

Optional cluster assignments per id. If unspecified, a matrix is returned containing the cluster-specific predictions per column.

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

-
cluster
+
cluster

The cluster name (as character) to predict for.

-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

@@ -180,15 +182,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-funFEM.html b/reference/interface-funFEM.html index 1eb83c09..1cb9b6e7 100644 --- a/reference/interface-funFEM.html +++ b/reference/interface-funFEM.html @@ -3,7 +3,7 @@ - +
- +

funFEM interface

- Source: R/methodFunFEM.R, R/modelFunFEM.R + Source: R/methodFunFEM.R, R/modelFunFEM.R
@@ -66,31 +66,31 @@

funFEM interface

- 
# S4 method for lcMethodFunFEM
+    
# S4 method for class 'lcMethodFunFEM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodFunFEM
+# S4 method for class 'lcMethodFunFEM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodFunFEM
+# S4 method for class 'lcMethodFunFEM'
 getCitation(object, ...)
 
-# S4 method for lcMethodFunFEM
+# S4 method for class 'lcMethodFunFEM'
 getName(object)
 
-# S4 method for lcMethodFunFEM
+# S4 method for class 'lcMethodFunFEM'
 getShortName(object)
 
-# S4 method for lcMethodFunFEM
+# S4 method for class 'lcMethodFunFEM'
 preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodFunFEM
+# S4 method for class 'lcMethodFunFEM'
 fit(method, data, envir, verbose, ...)
 
-# S3 method for lcModelFunFEM
+# S3 method for class 'lcModelFunFEM'
 fitted(object, ..., clusters = trajectoryAssignments(object))
 
-# S4 method for lcModelFunFEM
+# S4 method for class 'lcModelFunFEM'
 predictForCluster(
   object,
   newdata,
@@ -100,62 +100,64 @@ 
funFEM interface

   ...
 )
 
-# S4 method for lcModelFunFEM
+# S4 method for class 'lcModelFunFEM'
 postprob(object, ...)
 
-# S3 method for lcModelFunFEM
+# S3 method for class 'lcModelFunFEM'
 coef(object, ...)
 
-# S3 method for lcModelFunFEM
+# S3 method for class 'lcModelFunFEM'
 logLik(object, ...)
 
-# S4 method for lcModelFunFEM
+# S4 method for class 'lcModelFunFEM'
 converged(object, ...)

Arguments

-
object
+ + +
object

The lcModel object.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
clusters
+
clusters

Optional cluster assignments per id. If unspecified, a matrix is returned containing the cluster-specific predictions per column.

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

-
cluster
+
cluster

The cluster name (as character) to predict for.

-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

-
approxFun
+
approxFun

Function to interpolate between measurement moments, approx() by default.

@@ -176,15 +178,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-kml.html b/reference/interface-kml.html index 0a190ea5..d99ea87a 100644 --- a/reference/interface-kml.html +++ b/reference/interface-kml.html @@ -3,7 +3,7 @@ - +
- +

kml interface

- Source: R/methodKML.R, R/modelKML.R + Source: R/methodKML.R, R/modelKML.R
@@ -66,74 +66,76 @@

kml interface

- 
# S4 method for lcMethodKML
+    
# S4 method for class 'lcMethodKML'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodKML
+# S4 method for class 'lcMethodKML'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodKML
+# S4 method for class 'lcMethodKML'
 getCitation(object, ...)
 
-# S4 method for lcMethodKML
+# S4 method for class 'lcMethodKML'
 getName(object)
 
-# S4 method for lcMethodKML
+# S4 method for class 'lcMethodKML'
 getShortName(object)
 
-# S4 method for lcMethodKML
+# S4 method for class 'lcMethodKML'
 preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodKML
+# S4 method for class 'lcMethodKML'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcModelKML
+# S4 method for class 'lcModelKML'
 clusterTrajectories(object, at = time(object), ...)
 
-# S4 method for lcModelKML
+# S4 method for class 'lcModelKML'
 converged(object)
 
-# S3 method for lcModelKML
+# S3 method for class 'lcModelKML'
 logLik(object, ...)
 
-# S4 method for lcModelKML
+# S4 method for class 'lcModelKML'
 postprob(object)
 
-# S4 method for lcModelKML
+# S4 method for class 'lcModelKML'
 predictPostprob(object, newdata = NULL, ...)

Arguments

-
object
+ + +
object

The model.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing additional data variables returned by prepareData().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
at
+
at

A numeric vector of the times at which to compute the cluster trajectories.

-
newdata
+
newdata

Optional data.frame for which to compute the posterior probability. If omitted, the model training data is used.

@@ -154,15 +156,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-lcmm.html b/reference/interface-lcmm.html index f4f60361..14ff692b 100644 --- a/reference/interface-lcmm.html +++ b/reference/interface-lcmm.html @@ -3,7 +3,7 @@ - +
- +

lcmm interface

- Source: R/methodLcmmGMM.R, R/methodLcmmGBTM.R, R/modelLcmmGMM.R + Source: R/methodLcmmGMM.R, R/methodLcmmGBTM.R, R/modelLcmmGMM.R
@@ -66,121 +66,123 @@

lcmm interface

- 
# S4 method for lcMethodLcmmGMM
+    
# S4 method for class 'lcMethodLcmmGMM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodLcmmGMM
+# S4 method for class 'lcMethodLcmmGMM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodLcmmGMM
+# S4 method for class 'lcMethodLcmmGMM'
 getCitation(object, ...)
 
-# S4 method for lcMethodLcmmGMM
+# S4 method for class 'lcMethodLcmmGMM'
 getName(object)
 
-# S4 method for lcMethodLcmmGMM
+# S4 method for class 'lcMethodLcmmGMM'
 getShortName(object)
 
-# S4 method for lcMethodLcmmGMM
+# S4 method for class 'lcMethodLcmmGMM'
 validate(method, data, envir = NULL, ...)
 
-# S4 method for lcMethodLcmmGMM
+# S4 method for class 'lcMethodLcmmGMM'
 preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodLcmmGMM
+# S4 method for class 'lcMethodLcmmGMM'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodLcmmGMM
+# S4 method for class 'lcMethodLcmmGMM'
 responseVariable(object, ...)
 
-# S4 method for lcMethodLcmmGBTM
+# S4 method for class 'lcMethodLcmmGBTM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodLcmmGBTM
+# S4 method for class 'lcMethodLcmmGBTM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodLcmmGBTM
+# S4 method for class 'lcMethodLcmmGBTM'
 getCitation(object, ...)
 
-# S4 method for lcMethodLcmmGBTM
+# S4 method for class 'lcMethodLcmmGBTM'
 getName(object)
 
-# S4 method for lcMethodLcmmGBTM
+# S4 method for class 'lcMethodLcmmGBTM'
 getShortName(object)
 
-# S4 method for lcMethodLcmmGBTM
+# S4 method for class 'lcMethodLcmmGBTM'
 preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodLcmmGBTM
+# S4 method for class 'lcMethodLcmmGBTM'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodLcmmGBTM
+# S4 method for class 'lcMethodLcmmGBTM'
 responseVariable(object, ...)
 
-# S3 method for lcModelLcmmGMM
+# S3 method for class 'lcModelLcmmGMM'
 fitted(object, ..., clusters = trajectoryAssignments(object))
 
-# S4 method for lcModelLcmmGMM
+# S4 method for class 'lcModelLcmmGMM'
 predictForCluster(object, newdata, cluster, what = "mu", ...)
 
-# S3 method for lcModelLcmmGMM
+# S3 method for class 'lcModelLcmmGMM'
 model.matrix(object, ..., what = "mu")
 
-# S3 method for lcModelLcmmGMM
+# S3 method for class 'lcModelLcmmGMM'
 logLik(object, ...)
 
-# S3 method for lcModelLcmmGMM
+# S3 method for class 'lcModelLcmmGMM'
 sigma(object, ...)
 
-# S3 method for lcModelLcmmGMM
+# S3 method for class 'lcModelLcmmGMM'
 nobs(object, ...)
 
-# S4 method for lcModelLcmmGMM
+# S4 method for class 'lcModelLcmmGMM'
 postprob(object, ...)
 
-# S4 method for lcModelLcmmGMM
+# S4 method for class 'lcModelLcmmGMM'
 converged(object, ...)

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
clusters
+
clusters

Optional cluster assignments per id. If unspecified, a matrix is returned containing the cluster-specific predictions per column.

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

-
cluster
+
cluster

The cluster name (as character) to predict for.

-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

@@ -201,15 +203,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-mclust.html b/reference/interface-mclust.html index 434e5206..79b26c46 100644 --- a/reference/interface-mclust.html +++ b/reference/interface-mclust.html @@ -3,7 +3,7 @@ - +
- +

mclust interface

- Source: R/methodMclustLLPA.R, R/modelMclustLLPA.R + Source: R/methodMclustLLPA.R, R/modelMclustLLPA.R
@@ -66,74 +66,76 @@

mclust interface

- 
# S4 method for lcMethodMclustLLPA
+    
# S4 method for class 'lcMethodMclustLLPA'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodMclustLLPA
+# S4 method for class 'lcMethodMclustLLPA'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodMclustLLPA
+# S4 method for class 'lcMethodMclustLLPA'
 getCitation(object, ...)
 
-# S4 method for lcMethodMclustLLPA
+# S4 method for class 'lcMethodMclustLLPA'
 getName(object)
 
-# S4 method for lcMethodMclustLLPA
+# S4 method for class 'lcMethodMclustLLPA'
 getShortName(object)
 
-# S4 method for lcMethodMclustLLPA
+# S4 method for class 'lcMethodMclustLLPA'
 prepareData(method, data, verbose, ...)
 
-# S4 method for lcMethodMclustLLPA
+# S4 method for class 'lcMethodMclustLLPA'
 compose(method, envir = NULL)
 
-# S4 method for lcMethodMclustLLPA
+# S4 method for class 'lcMethodMclustLLPA'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcModelMclustLLPA
+# S4 method for class 'lcModelMclustLLPA'
 clusterTrajectories(object, at = time(object), ...)
 
-# S4 method for lcModelMclustLLPA
+# S4 method for class 'lcModelMclustLLPA'
 postprob(object, ...)
 
-# S4 method for lcModelMclustLLPA
+# S4 method for class 'lcModelMclustLLPA'
 predictPostprob(object, newdata = NULL, ...)
 
-# S4 method for lcModelMclustLLPA
+# S4 method for class 'lcModelMclustLLPA'
 converged(object, ...)

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
at
+
at

A numeric vector of the times at which to compute the cluster trajectories.

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

@@ -154,15 +156,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-metaMethods.html b/reference/interface-metaMethods.html index 8334406f..05fcafef 100644 --- a/reference/interface-metaMethods.html +++ b/reference/interface-metaMethods.html @@ -3,7 +3,7 @@ - +
- +

lcMetaMethod abstract class

- Source: R/meta-method.R, R/meta-fit-converged.R, R/meta-fit-rep.R + Source: R/meta-method.R, R/meta-fit-converged.R, R/meta-fit-rep.R
@@ -66,85 +66,87 @@

lcMetaMethod abstract class

- 
# S4 method for lcMetaMethod
+    
# S4 method for class 'lcMetaMethod'
 compose(method, envir = NULL)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 getLcMethod(object, ...)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 getName(object, ...)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 getShortName(object, ...)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 idVariable(object, ...)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 preFit(method, data, envir, verbose)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 prepareData(method, data, verbose)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 fit(method, data, envir, verbose)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 postFit(method, data, model, envir, verbose)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 responseVariable(object, ...)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 timeVariable(object, ...)
 
-# S4 method for lcMetaMethod
+# S4 method for class 'lcMetaMethod'
 validate(method, data, envir = NULL, ...)
 
-# S3 method for lcMetaMethod
+# S3 method for class 'lcMetaMethod'
 update(object, ...)
 
-# S4 method for lcFitConverged
+# S4 method for class 'lcFitConverged'
 fit(method, data, envir, verbose)
 
-# S4 method for lcFitConverged
+# S4 method for class 'lcFitConverged'
 validate(method, data, envir = NULL, ...)
 
-# S4 method for lcFitRep
+# S4 method for class 'lcFitRep'
 fit(method, data, envir, verbose)
 
-# S4 method for lcFitRep
+# S4 method for class 'lcFitRep'
 validate(method, data, envir = NULL, ...)

Arguments

-
method
+ + +
method

The lcMethod object.

-
envir
+
envir

The environment in which the lcMethod should be evaluated

-
object
+
object

The model.

-
...
+
...

Not used.

-
data
+
data

A data.frame representing the transformed training data.

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
model
+
model

The lcModel object returned by fit().

@@ -161,15 +163,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-mixAK.html b/reference/interface-mixAK.html index dd2a6858..1f9a0af8 100644 --- a/reference/interface-mixAK.html +++ b/reference/interface-mixAK.html @@ -3,7 +3,7 @@ - +
- +

mixAK interface

- Source: R/methodMixAK_GLMM.R, R/modelMixAK_GLMM.R, R/modelMixAK_GLMMlist.R + Source: R/methodMixAK_GLMM.R, R/modelMixAK_GLMM.R, R/modelMixAK_GLMMlist.R
@@ -66,91 +66,93 @@

mixAK interface

- 
# S4 method for lcMethodMixAK_GLMM
+    
# S4 method for class 'lcMethodMixAK_GLMM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodMixAK_GLMM
+# S4 method for class 'lcMethodMixAK_GLMM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodMixAK_GLMM
+# S4 method for class 'lcMethodMixAK_GLMM'
 getCitation(object, ...)
 
-# S4 method for lcMethodMixAK_GLMM
+# S4 method for class 'lcMethodMixAK_GLMM'
 getName(object)
 
-# S4 method for lcMethodMixAK_GLMM
+# S4 method for class 'lcMethodMixAK_GLMM'
 getShortName(object)
 
-# S4 method for lcMethodMixAK_GLMM
+# S4 method for class 'lcMethodMixAK_GLMM'
 responseVariable(object)
 
-# S4 method for lcMethodMixAK_GLMM
+# S4 method for class 'lcMethodMixAK_GLMM'
 preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodMixAK_GLMM
+# S4 method for class 'lcMethodMixAK_GLMM'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcModelMixAK_GLMM
+# S4 method for class 'lcModelMixAK_GLMM'
 postprob(object, ...)
 
-# S4 method for lcModelMixAK_GLMM
+# S4 method for class 'lcModelMixAK_GLMM'
 predictForCluster(object, newdata, cluster, what = "mu", ...)
 
-# S4 method for lcModelMixAK_GLMM
+# S4 method for class 'lcModelMixAK_GLMM'
 predictForCluster(object, newdata, cluster, what = "mu", ...)
 
-# S3 method for lcModelMixAK_GLMM
+# S3 method for class 'lcModelMixAK_GLMM'
 coef(object, ..., stat = "Mean")
 
-# S3 method for lcModelMixAK_GLMM
+# S3 method for class 'lcModelMixAK_GLMM'
 deviance(object, ...)
 
-# S4 method for lcModelMixAK_GLMMlist
+# S4 method for class 'lcModelMixAK_GLMMlist'
 postprob(object, ...)
 
-# S4 method for lcModelMixAK_GLMMlist
+# S4 method for class 'lcModelMixAK_GLMMlist'
 predictForCluster(object, newdata, cluster, what = "mu", ...)

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

-
cluster
+
cluster

The cluster name (as character) to predict for.

-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

-
stat
+
stat

The aggregate statistic to extract. The mean is used by default.

@@ -171,15 +173,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-mixtools.html b/reference/interface-mixtools.html index 6bb20505..66154d8c 100644 --- a/reference/interface-mixtools.html +++ b/reference/interface-mixtools.html @@ -3,7 +3,7 @@ - +
- +

mixtools interface

- Source: R/methodMixtoolsGMM.R, R/methodMixtoolsNPRM.R, R/modelMixtoolsGMM.R, and 1 more + Source: R/methodMixtoolsGMM.R, R/methodMixtoolsNPRM.R, R/modelMixtoolsGMM.R, and 1 more
@@ -66,61 +66,61 @@

mixtools interface

- 
# S4 method for lcMethodMixtoolsGMM
+    
# S4 method for class 'lcMethodMixtoolsGMM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodMixtoolsGMM
+# S4 method for class 'lcMethodMixtoolsGMM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodMixtoolsGMM
+# S4 method for class 'lcMethodMixtoolsGMM'
 getCitation(object, ...)
 
-# S4 method for lcMethodMixtoolsGMM
+# S4 method for class 'lcMethodMixtoolsGMM'
 getName(object)
 
-# S4 method for lcMethodMixtoolsGMM
+# S4 method for class 'lcMethodMixtoolsGMM'
 getShortName(object)
 
-# S4 method for lcMethodMixtoolsGMM
+# S4 method for class 'lcMethodMixtoolsGMM'
 preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodMixtoolsGMM
+# S4 method for class 'lcMethodMixtoolsGMM'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodMixtoolsNPRM
+# S4 method for class 'lcMethodMixtoolsNPRM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodMixtoolsNPRM
+# S4 method for class 'lcMethodMixtoolsNPRM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodMixtoolsNPRM
+# S4 method for class 'lcMethodMixtoolsNPRM'
 getCitation(object, ...)
 
-# S4 method for lcMethodMixtoolsNPRM
+# S4 method for class 'lcMethodMixtoolsNPRM'
 getName(object)
 
-# S4 method for lcMethodMixtoolsNPRM
+# S4 method for class 'lcMethodMixtoolsNPRM'
 getShortName(object)
 
-# S4 method for lcMethodMixtoolsNPRM
+# S4 method for class 'lcMethodMixtoolsNPRM'
 fit(method, data, envir, verbose, ...)
 
-# S4 method for lcModelMixtoolsGMM
+# S4 method for class 'lcModelMixtoolsGMM'
 predictForCluster(object, newdata, cluster, what = "mu", ...)
 
-# S4 method for lcModelMixtoolsGMM
+# S4 method for class 'lcModelMixtoolsGMM'
 postprob(object, ...)
 
-# S3 method for lcModelMixtoolsGMM
+# S3 method for class 'lcModelMixtoolsGMM'
 logLik(object, ...)
 
-# S3 method for lcModelMixtoolsGMM
+# S3 method for class 'lcModelMixtoolsGMM'
 coef(object, ...)
 
-# S3 method for lcModelMixtoolsGMM
+# S3 method for class 'lcModelMixtoolsGMM'
 sigma(object, ...)
 
-# S4 method for lcModelMixtoolsRM
+# S4 method for class 'lcModelMixtoolsRM'
 clusterTrajectories(
   object,
   at = time(object),
@@ -130,63 +130,65 @@ 
mixtools interface

   ...
 )
 
-# S4 method for lcModelMixtoolsRM
+# S4 method for class 'lcModelMixtoolsRM'
 postprob(object, ...)
 
-# S3 method for lcModelMixtoolsRM
+# S3 method for class 'lcModelMixtoolsRM'
 logLik(object, ...)
 
-# S4 method for lcModelMixtoolsRM
+# S4 method for class 'lcModelMixtoolsRM'
 converged(object, ...)

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

-
cluster
+
cluster

The cluster name (as character) to predict for.

-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

-
at
+
at

A numeric vector of the times at which to compute the cluster trajectories.

-
se
+
se

Whether to compute the standard error of the prediction.

-
ci
+
ci

The confidence interval to compute.

@@ -207,15 +209,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/interface-mixtvem.html b/reference/interface-mixtvem.html index 2429926c..9d24f56a 100644 --- a/reference/interface-mixtvem.html +++ b/reference/interface-mixtvem.html @@ -3,7 +3,7 @@ - +
- +

mixtvem interface

- Source: R/methodMixTVEM.R, R/modelMixTVEM.R + Source: R/methodMixTVEM.R, R/modelMixTVEM.R
@@ -66,78 +66,80 @@

mixtvem interface

- 
# S4 method for lcMethodMixTVEM
+    
# S4 method for class 'lcMethodMixTVEM'
 getArgumentDefaults(object)
 
-# S4 method for lcMethodMixTVEM
+# S4 method for class 'lcMethodMixTVEM'
 getArgumentExclusions(object)
 
-# S4 method for lcMethodMixTVEM
+# S4 method for class 'lcMethodMixTVEM'
 getCitation(object, ...)
 
-# S4 method for lcMethodMixTVEM
+# S4 method for class 'lcMethodMixTVEM'
 getName(object)
 
-# S4 method for lcMethodMixTVEM
+# S4 method for class 'lcMethodMixTVEM'
 getShortName(object)
 
-# S4 method for lcMethodMixTVEM
+# S4 method for class 'lcMethodMixTVEM'
 preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethodMixTVEM
+# S4 method for class 'lcMethodMixTVEM'
 fit(method, data, envir, verbose, ...)
 
-# S3 method for lcModelMixTVEM
+# S3 method for class 'lcModelMixTVEM'
 predict(object, ..., newdata = NULL, what = "mu")
 
-# S4 method for lcModelMixTVEM
+# S4 method for class 'lcModelMixTVEM'
 postprob(object, ...)
 
-# S4 method for lcModelMixTVEM
+# S4 method for class 'lcModelMixTVEM'
 converged(object, ...)
 
-# S3 method for lcModelMixTVEM
+# S3 method for class 'lcModelMixTVEM'
 logLik(object, ...)
 
-# S3 method for lcModelMixTVEM
+# S3 method for class 'lcModelMixTVEM'
 sigma(object, ...)
 
-# S3 method for lcModelMixTVEM
+# S3 method for class 'lcModelMixTVEM'
 coef(object, ...)

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Not used.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
newdata
+
newdata

Optional data.frame for which to compute the model predictions. If omitted, the model training data is used. Cluster trajectory predictions are made when ids are not specified.

-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

@@ -158,15 +160,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/is.html b/reference/is.html index 8ad10c70..a4bd547e 100644 --- a/reference/is.html +++ b/reference/is.html @@ -3,7 +3,7 @@ - +
- +

Check if object is of Class

- Source: R/method.R, R/model.R, R/models.R + Source: R/method.R, R/model.R, R/models.R
@@ -75,17 +75,15 @@

Check if object is of Class

Arguments

-
x
+ + +
x

The object to check the class of.

Value

- - -

scalar logical

- - +

scalar logical

@@ -100,15 +98,15 @@

Value

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/isArgDefined.html b/reference/isArgDefined.html index 87d2504b..2a507e3a 100644 --- a/reference/isArgDefined.html +++ b/reference/isArgDefined.html @@ -3,7 +3,7 @@ - +
- +

Check whether the argument of a lcMethod has a defined value.

- Source: R/method.R + Source: R/method.R
@@ -71,15 +71,17 @@

Check whether the argument of a lcMethod has a defined value.

Arguments

-
object
+ + +
object

The lcMethod object.

-
name
+
name

The name of the argument, as character.

-
envir
+
envir

The environment to evaluate the arguments in. If NULL, the argument is not evaluated.

@@ -96,15 +98,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrend-approaches.html b/reference/latrend-approaches.html index a92a0be7..817150c5 100644 --- a/reference/latrend-approaches.html +++ b/reference/latrend-approaches.html @@ -2,22 +2,17 @@ High-level approaches to longitudinal clustering — latrend-approaches • latrend - +
- +

High-level approaches to longitudinal clustering

- Source: R/methods.R + Source: R/methods.R

This page provides high-level guidelines on which methods are applicable to your dataset. Note that this is intended as a quick-start.

-

Recommended overview and comparison papers:

  • (Den Teuling et al. 2021) -: A tutorial and overview on methods for longitudinal clustering.

    • -

  • Den Teuling et al. (2021) - compared KmL, MixTVEM, GBTM, GMM, and GCKM.

    • -

  • Twisk and Hoekstra (2012) - compared KmL, GCKM, LLCA, GBTM and GMM.

    • -

  • Verboon and Pat-El (2022) - compared the kml, traj and lcmm packages in R.

    • -

  • Martin and von Oertzen (2015) - compared KmL, LCA, and GMM.

    • +

    Recommended overview and comparison papers:

    • denteuling2021clusteringlatrend: A tutorial and overview on methods for longitudinal clustering.

      • +

    • denteuling2021comparison;textuallatrend compared KmL, MixTVEM, GBTM, GMM, and GCKM.

      • +

    • twisk2012classifying;textuallatrend compared KmL, GCKM, LLCA, GBTM and GMM.

      • +

    • verboon2022clustering;textuallatrend compared the kml, traj and lcmm packages in R.

      • +

    • martin2015growth;textuallatrend compared KmL, LCA, and GMM.

Approaches

- -

Disclaimer: The table below has been adapted from a pre-print of (Den Teuling et al. 2021) -.

ApproachStrengthsLimitationsMethods
Cross-sectional clusteringSuitable for large datasets — Many available algorithms — Non-parametric cluster trajectory representationRequires time-aligned complete data — Sensitive to measurement noiselcMethodKML lcMethodMclustLLPA lcMethodMixtoolsNPRM
Distance-based clusteringSuitable for medium-sized datasets — Many distance metrics — Distance matrix only needs to be computed onceScales poorly with number of trajectories — No robust cluster trajectory representation — Some distance metrics require aligned observationslcMethodDtwclust
Feature-based clusteringSuitable for large datasets — Configurable — Features only needs to be computed once — Compact trajectory representationGenerally requires intensive longitudinal data — Sensitive to outlierslcMethodFeature lcMethodAkmedoids lcMethodLMKM lcMethodGCKM
Model-based clusteringParametric cluster trajectory — Incorporate (domain) assumptions — Low sample size requirementsComputationally intensive — Scales poorly with number of clusters — Convergence challengeslcMethodLcmmGBTM lcMethodLcmmGMM lcMethodCrimCV lcMethodFlexmix lcMethodFlexmixGBTM lcMethodFunFEM lcMethodMixAK_GLMM lcMethodMixtoolsGMM lcMethodMixTVEM

It is strongly encouraged to evaluate and compare several candidate methods in order to identify the most suitable method.

+ +

Disclaimer: The table below has been adapted from a pre-print of denteuling2021clusteringlatrend.

ApproachStrengthsLimitationsMethods
Cross-sectional clusteringSuitable for large datasets — Many available algorithms — Non-parametric cluster trajectory representationRequires time-aligned complete data — Sensitive to measurement noiselcMethodKML lcMethodMclustLLPA lcMethodMixtoolsNPRM
Distance-based clusteringSuitable for medium-sized datasets — Many distance metrics — Distance matrix only needs to be computed onceScales poorly with number of trajectories — No robust cluster trajectory representation — Some distance metrics require aligned observationslcMethodDtwclust
Feature-based clusteringSuitable for large datasets — Configurable — Features only needs to be computed once — Compact trajectory representationGenerally requires intensive longitudinal data — Sensitive to outlierslcMethodFeature lcMethodAkmedoids lcMethodLMKM lcMethodGCKM
Model-based clusteringParametric cluster trajectory — Incorporate (domain) assumptions — Low sample size requirementsComputationally intensive — Scales poorly with number of clusters — Convergence challengeslcMethodLcmmGBTM lcMethodLcmmGMM lcMethodCrimCV lcMethodFlexmix lcMethodFlexmixGBTM lcMethodFunFEM lcMethodMixAK_GLMM lcMethodMixtoolsGMM lcMethodMixTVEM

It is strongly encouraged to evaluate and compare several candidate methods in order to identify the most suitable method.

References

-

Den Teuling N, Pauws S, Heuvel Evd (2021). -“Clustering of longitudinal data: A tutorial on a variety of approaches.” -doi:10.48550/ARXIV.2111.05469 -, https://arxiv.org/abs/2111.05469.

Den Teuling NGP, Pauws SC, van den Heuvel ER (2021). -“A comparison of methods for clustering longitudinal data with slowly changing trends.” -Communications in Statistics - Simulation and Computation. -doi:10.1080/03610918.2020.1861464 -.

Martin DP, von Oertzen T (2015). -“Growth mixture models outperform simpler clustering algorithms when detecting longitudinal heterogeneity, even with small sample sizes.” -Struct. Equ. Model., 22(2), 264--275. -ISSN 1070-5511, doi:10.1080/10705511.2014.936340 -.

Twisk J, Hoekstra T (2012). -“Classifying developmental trajectories over time should be done with great caution: A comparison between methods.” -Journal of Clinical Epidemiology, 65(10), 1078--1087. -ISSN 0895-4356, doi:10.1016/j.jclinepi.2012.04.010 -.

Verboon P, Pat-El R (2022). -“Clustering Longitudinal Data Using R: A Monte Carlo Study.” -Methodology, 18(2), 144-163. -doi:10.5964/meth.7143 -.

+

See also

@@ -138,15 +108,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrend-data.html b/reference/latrend-data.html index 7e55dbeb..93022cd0 100644 --- a/reference/latrend-data.html +++ b/reference/latrend-data.html @@ -13,7 +13,7 @@ - +
- +

Longitudinal dataset representation

- Source: R/data.R + Source: R/data.R
@@ -85,7 +85,7 @@

Longitudinal dataset representation

Included longitudinal datasets

- +

The following datasets are included with the package:

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrend-estimation.html b/reference/latrend-estimation.html index 82e7bb40..515bb626 100644 --- a/reference/latrend-estimation.html +++ b/reference/latrend-estimation.html @@ -4,7 +4,7 @@ - +
- +

Overview of lcMethod estimation functions

- Source: R/latrend.R + Source: R/latrend.R
@@ -70,7 +70,7 @@

Overview of lcMethod estimation functions

latrend estimation functions

- +

Parallel estimation

- +

The functions involving repeated estimation support parallel computation. See here.

@@ -101,15 +101,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrend-generics.html b/reference/latrend-generics.html index e592cd0b..8a9b4284 100644 --- a/reference/latrend-generics.html +++ b/reference/latrend-generics.html @@ -3,7 +3,7 @@ - +
- +

Generics used by latrend for different classes

- Source: R/zzz.R + Source: R/zzz.R
@@ -79,15 +79,15 @@

Generics used by latrend for different classes

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrend-methods.html b/reference/latrend-methods.html index 456e59c4..daf0dcff 100644 --- a/reference/latrend-methods.html +++ b/reference/latrend-methods.html @@ -4,7 +4,7 @@ - +
- +

Supported methods for longitudinal clustering

- Source: R/methods.R + Source: R/methods.R
@@ -70,42 +70,14 @@

Supported methods for longitudinal clustering

Supported methods

- -
MethodDescriptionSource
lcMethodAkmedoidsAnchored k-medoids (Adepeju et al. 2020)akmedoids
lcMethodCrimCVGroup-based trajectory modeling of count data (Nielsen 2018)crimCV
lcMethodDtwclustMethods for distance-based clustering, including dynamic time warping (Sardá-Espinosa 2019)dtwclust
lcMethodFeatureFeature-based clustering
lcMethodFlexmixInterface to the FlexMix framework (Grün and Leisch 2008)flexmix
lcMethodFlexmixGBTMGroup-based trajectory modelingflexmix
lcMethodFunFEMModel-based clustering using funFEM (Bouveyron 2015)funFEM
lcMethodGCKMGrowth-curve modeling and k-meanslme4
lcMethodKMLLongitudinal k-means (Genolini et al. 2015)kml
lcMethodLcmmGBTMGroup-based trajectory modeling (Proust-Lima et al. 2017)lcmm
lcMethodLcmmGMMGrowth mixture modeling (Proust-Lima et al. 2017)lcmm
lcMethodLMKMFeature-based clustering using linear regression and k-means
lcMethodMclustLLPALongitudinal latent profile analysis (Scrucca et al. 2016)mclust
lcMethodMixAK_GLMMMixture of generalized linear mixed modelsmixAK
lcMethodMixtoolsGMMGrowth mixture modelingmixtools
lcMethodMixtoolsNPRMNon-parametric repeated measures clustering (Benaglia et al. 2009)mixtools
lcMethodMixTVEMMixture of time-varying effects models
lcMethodRandomRandom partitioning
lcMethodStratifyStratification rule

In addition, the functionality of any method can be extended via meta methods. + +
MethodDescriptionSource
lcMethodAkmedoidsAnchored k-medoids adepeju2020akmedoidslatrendakmedoids
lcMethodCrimCVGroup-based trajectory modeling of count data nielsen2018crimcvlatrendcrimCV
lcMethodDtwclustMethods for distance-based clustering, including dynamic time warping sardaespinosa2019timelatrenddtwclust
lcMethodFeatureFeature-based clustering
lcMethodFlexmixInterface to the FlexMix framework gruen2008flexmixlatrendflexmix
lcMethodFlexmixGBTMGroup-based trajectory modelingflexmix
lcMethodFunFEMModel-based clustering using funFEM bouveyron2015funfemlatrendfunFEM
lcMethodGCKMGrowth-curve modeling and k-meanslme4
lcMethodKMLLongitudinal k-means genolini2015kmllatrendkml
lcMethodLcmmGBTMGroup-based trajectory modeling proustlima2017estimationlatrendlcmm
lcMethodLcmmGMMGrowth mixture modeling proustlima2017estimationlatrendlcmm
lcMethodLMKMFeature-based clustering using linear regression and k-means
lcMethodMclustLLPALongitudinal latent profile analysis scrucca2016mclustlatrendmclust
lcMethodMixAK_GLMMMixture of generalized linear mixed modelsmixAK
lcMethodMixtoolsGMMGrowth mixture modelingmixtools
lcMethodMixtoolsNPRMNon-parametric repeated measures clustering benaglia2009mixtoolslatrendmixtools
lcMethodMixTVEMMixture of time-varying effects models
lcMethodRandomRandom partitioning
lcMethodStratifyStratification rule

In addition, the functionality of any method can be extended via meta methods. This is used for extending the estimation procedure of a method, such as repeated fitting and selecting the best result, or fitting until convergence.

It is strongly encouraged to evaluate and compare several candidate methods in order to identify the most suitable method.

References

-

Adepeju M, Langton S, Bannister J (2020). -akmedoids: Anchored Kmedoids for Longitudinal Data Clustering. -R package version 0.1.5, https://CRAN.R-project.org/package=akmedoids.

Benaglia T, Chauveau D, Hunter DR, Young D (2009). -“mixtools: An R Package for Analyzing Finite Mixture Models.” -Journal of Statistical Software, 32(6), 1--29. -doi:10.18637/jss.v032.i06 -.

Bouveyron C (2015). -funFEM: Clustering in the Discriminative Functional Subspace. -R package version 1.1, https://CRAN.R-project.org/package=funFEM.

Genolini C, Alacoque X, Sentenac M, Arnaud C (2015). -“kml and kml3d: R Packages to Cluster Longitudinal Data.” -Journal of Statistical Software, 65(4), 1--34. -doi:10.18637/jss.v065.i04 -.

Grün B, Leisch F (2008). -“FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters.” -Journal of Statistical Software, 28(4), 1--35. -doi:10.18637/jss.v028.i04 -.

Nielsen JD (2018). -crimCV: Group-Based Modelling of Longitudinal Data. -R package version 0.9.6, https://CRAN.R-project.org/package=crimCV.

Proust-Lima C, Philipps V, Liquet B (2017). -“Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm.” -Journal of Statistical Software, 78(2), 1--56. -doi:10.18637/jss.v078.i02 -.

Sardá-Espinosa A (2019). -“Time-Series Clustering in R Using the dtwclust Package.” -The R Journal. -doi:10.32614/RJ-2019-023 -.

Scrucca L, Fop M, Murphy TB, Raftery AE (2016). -“mclust 5: clustering, classification and density estimation using Gaussian finite mixture models.” -The R Journal, 8(1), 205--233.

+

See also

@@ -131,15 +103,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrend-metrics.html b/reference/latrend-metrics.html index ba5eaa53..4beba312 100644 --- a/reference/latrend-metrics.html +++ b/reference/latrend-metrics.html @@ -9,7 +9,7 @@ - +
- +

Metrics

- Source: R/metrics.R + Source: R/metrics.R
@@ -77,13 +77,12 @@

Metrics

Supported internal metrics

- -
Metric nameDescriptionFunction / Reference
AICAkaike information criterion. A goodness-of-fit estimator that adjusts for model complexity (i.e., the number of parameters). Only available for models that support the computation of the model log-likelihood through logLik.stats::AIC(), (Akaike 1974)
APPA.meanMean of the average posterior probability of assignment (APPA) across clusters. A measure of the precision of the trajectory classifications. A score of 1 indicates perfect classification.APPA(), (Nagin 2005)
APPA.minLowest APPA among the clustersAPPA(), (Nagin 2005)
ASWAverage silhouette width based on the Euclidean distance(Rousseeuw 1987)
BICBayesian information criterion. A goodness-of-fit estimator that corrects for the degrees of freedom (i.e., the number of parameters) and sample size. Only available for models that support the computation of the model log-likelihood through logLik.stats::BIC(), (Schwarz 1978)
CAICConsistent Akaike information criterion(Bozdogan 1987)
CLCClassification likelihood criterion(McLachlan and Peel 2000)
convergedWhether the model converged during estimationconverged()
devianceThe model deviancestats::deviance()
DunnThe Dunn index(Dunn 1974)
entropyEntropy of the posterior probabilities
estimationTimeThe time needed for fitting the modelestimationTime()
EDEuclidean distance between the cluster trajectories and the assigned observed trajectories
ED.fitEuclidean distance between the cluster trajectories and the assigned fitted trajectories
ICL.BICIntegrated classification likelihood (ICL) approximated using the BIC(Biernacki et al. 2000)
logLikModel log-likelihoodstats::logLik()
MAEMean absolute error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
MahalanobisMahalanobis distance between the cluster trajectories and the assigned observed trajectories(Mahalanobis 1936)
MSEMean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
relativeEntropy, REA measure of the precision of the trajectory classification. A value of 1 indicates perfect classification, whereas a value of 0 indicates a non-informative uniform classification. It is the normalized version of entropy, scaled between [0, 1].(Ramaswamy et al. 1993) -, (Muthén 2004)
RMSERoot mean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
RSSResidual sum of squares under most likely cluster allocation
scaledEntropySee relativeEntropy
sigmaThe residual standard deviationstats::sigma()
ssBICSample-size adjusted BIC(Sclove 1987)
SEDStandardized Euclidean distance between the cluster trajectories and the assigned observed trajectories
SED.fitThe cluster-weighted standardized Euclidean distance between the cluster trajectories and the assigned fitted trajectories
WMAEMAE weighted by cluster-assignment probability
WMSEMSE weighted by cluster-assignment probability
WRMSERMSE weighted by cluster-assignment probability
WRSSRSS weighted by cluster-assignment probability
+ +
Metric nameDescriptionFunction / Reference
AICAkaike information criterion. A goodness-of-fit estimator that adjusts for model complexity (i.e., the number of parameters). Only available for models that support the computation of the model log-likelihood through logLik.stats::AIC(), akaike1974newlatrend
APPA.meanMean of the average posterior probability of assignment (APPA) across clusters. A measure of the precision of the trajectory classifications. A score of 1 indicates perfect classification.APPA(), nagin2005grouplatrend
APPA.minLowest APPA among the clustersAPPA(), nagin2005grouplatrend
ASWAverage silhouette width based on the Euclidean distancerousseeuw1987silhouetteslatrend
BICBayesian information criterion. A goodness-of-fit estimator that corrects for the degrees of freedom (i.e., the number of parameters) and sample size. Only available for models that support the computation of the model log-likelihood through logLik.stats::BIC(), schwarz1978estimatinglatrend
CAICConsistent Akaike information criterionbozdogan1987modellatrend
CLCClassification likelihood criterionmclachlan2000finitelatrend
convergedWhether the model converged during estimationconverged()
devianceThe model deviancestats::deviance()
DunnThe Dunn indexdunn1974welllatrend
entropyEntropy of the posterior probabilities
estimationTimeThe time needed for fitting the modelestimationTime()
EDEuclidean distance between the cluster trajectories and the assigned observed trajectories
ED.fitEuclidean distance between the cluster trajectories and the assigned fitted trajectories
ICL.BICIntegrated classification likelihood (ICL) approximated using the BICbiernacki2000assessinglatrend
logLikModel log-likelihoodstats::logLik()
MAEMean absolute error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
MahalanobisMahalanobis distance between the cluster trajectories and the assigned observed trajectoriesmahalanobis1936generalizedlatrend
MSEMean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
relativeEntropy, REA measure of the precision of the trajectory classification. A value of 1 indicates perfect classification, whereas a value of 0 indicates a non-informative uniform classification. It is the normalized version of entropy, scaled between [0, 1].ramaswamy1993empiricallatrend, muthen2004latentlatrend
RMSERoot mean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
RSSResidual sum of squares under most likely cluster allocation
scaledEntropySee relativeEntropy
sigmaThe residual standard deviationstats::sigma()
ssBICSample-size adjusted BICsclove1987applicationlatrend
SEDStandardized Euclidean distance between the cluster trajectories and the assigned observed trajectories
SED.fitThe cluster-weighted standardized Euclidean distance between the cluster trajectories and the assigned fitted trajectories
WMAEMAE weighted by cluster-assignment probability
WMSEMSE weighted by cluster-assignment probability
WRMSERMSE weighted by cluster-assignment probability
WRSSRSS weighted by cluster-assignment probability

Supported external metrics

- -
Metric nameDescriptionFunction / Reference
adjustedRandAdjusted Rand index. Based on the Rand index, but adjusted for agreements occurring by chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.mclustcomp::mclustcomp(), (Hubert and Arabie 1985)
CohensKappaCohen's kappa. A partitioning agreement metric correcting for random chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.psych::cohen.kappa(), (Cohen 1960)
FF-scoremclustcomp::mclustcomp()
F1F1-score, also referred to as the Sørensen–Dice Coefficient, or Dice similarity coefficientmclustcomp::mclustcomp()
FolkesMallowsFowlkes-Mallows indexmclustcomp::mclustcomp()
HubertHubert indexclusterCrit::extCriteria()
JaccardJaccard indexmclustcomp::mclustcomp()
jointEntropyJoint entropy between model assignmentsmclustcomp::mclustcomp()
KulczynskiKulczynski indexclusterCrit::extCriteria()
MaximumMatchMaximum match measuremclustcomp::mclustcomp()
McNemarMcNemar statisticclusterCrit::extCriteria()
MeilaHeckermanMeila-Heckerman measuremclustcomp::mclustcomp()
MirkinMirkin metricmclustcomp::mclustcomp()
MIMutual informationmclustcomp::mclustcomp()
NMINormalized mutual informationigraph::compare()
NSJNormalized version of splitJoin. The proportion of edits relative to the maximum changes (twice the number of ids)
NVINormalized variation of informationmclustcomp::mclustcomp()
OverlapOverlap coefficient, also referred to as the Szymkiewicz–Simpson coefficientmclustcomp::mclustcomp() (M K and K 2016)
PDPartition differencemclustcomp::mclustcomp()
PhiPhi coefficient.clusterCrit::extCriteria()
precisionprecisionclusterCrit::extCriteria()
RandRand indexmclustcomp::mclustcomp()
recallrecallclusterCrit::extCriteria()
RogersTanimotoRogers-Tanimoto dissimilarityclusterCrit::extCriteria()
RusselRaoRussell-Rao dissimilarityclusterCrit::extCriteria()
SMCSimple matching coefficientmclustcomp::mclustcomp()
splitJointotal split-join indexigraph::split_join_distance()
splitJoin.refSplit-join index of the first model to the second model. In other words, it is the edit-distance between the two partitionings.
SokalSneath1Type-1 Sokal-Sneath dissimilarityclusterCrit::extCriteria()
SokalSneath2Type-2 Sokal-Sneath dissimilarityclusterCrit::extCriteria()
VIVariation of informationmclustcomp::mclustcomp()
Wallace1Type-1 Wallace criterionmclustcomp::mclustcomp()
Wallace2Type-2 Wallace criterionmclustcomp::mclustcomp()
WMSSEWeighted minimum sum of squared errors between cluster trajectories
WMMSEWeighted minimum mean of squared errors between cluster trajectories
WMMAEWeighted minimum mean of absolute errors between cluster trajectories
+ +
Metric nameDescriptionFunction / Reference
adjustedRandAdjusted Rand index. Based on the Rand index, but adjusted for agreements occurring by chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.mclustcomp::mclustcomp(), hubert1985comparinglatrend
CohensKappaCohen's kappa. A partitioning agreement metric correcting for random chance. A score of 1 indicates a perfect agreement, whereas a score of 0 indicates an agreement no better than chance.psych::cohen.kappa(), cohen1960coefficientlatrend
FF-scoremclustcomp::mclustcomp()
F1F1-score, also referred to as the Sørensen–Dice Coefficient, or Dice similarity coefficientmclustcomp::mclustcomp()
FolkesMallowsFowlkes-Mallows indexmclustcomp::mclustcomp()
HubertHubert indexclusterCrit::extCriteria()
JaccardJaccard indexmclustcomp::mclustcomp()
jointEntropyJoint entropy between model assignmentsmclustcomp::mclustcomp()
KulczynskiKulczynski indexclusterCrit::extCriteria()
MaximumMatchMaximum match measuremclustcomp::mclustcomp()
McNemarMcNemar statisticclusterCrit::extCriteria()
MeilaHeckermanMeila-Heckerman measuremclustcomp::mclustcomp()
MirkinMirkin metricmclustcomp::mclustcomp()
MIMutual informationmclustcomp::mclustcomp()
NMINormalized mutual informationigraph::compare()
NSJNormalized version of splitJoin. The proportion of edits relative to the maximum changes (twice the number of ids)
NVINormalized variation of informationmclustcomp::mclustcomp()
OverlapOverlap coefficient, also referred to as the Szymkiewicz–Simpson coefficientmclustcomp::mclustcomp() vijaymeena2016surveylatrend
PDPartition differencemclustcomp::mclustcomp()
PhiPhi coefficient.clusterCrit::extCriteria()
precisionprecisionclusterCrit::extCriteria()
RandRand indexmclustcomp::mclustcomp()
recallrecallclusterCrit::extCriteria()
RogersTanimotoRogers-Tanimoto dissimilarityclusterCrit::extCriteria()
RusselRaoRussell-Rao dissimilarityclusterCrit::extCriteria()
SMCSimple matching coefficientmclustcomp::mclustcomp()
splitJointotal split-join indexigraph::split_join_distance()
splitJoin.refSplit-join index of the first model to the second model. In other words, it is the edit-distance between the two partitionings.
SokalSneath1Type-1 Sokal-Sneath dissimilarityclusterCrit::extCriteria()
SokalSneath2Type-2 Sokal-Sneath dissimilarityclusterCrit::extCriteria()
VIVariation of informationmclustcomp::mclustcomp()
Wallace1Type-1 Wallace criterionmclustcomp::mclustcomp()
Wallace2Type-2 Wallace criterionmclustcomp::mclustcomp()
WMSSEWeighted minimum sum of squared errors between cluster trajectories
WMMSEWeighted minimum mean of squared errors between cluster trajectories
WMMAEWeighted minimum mean of absolute errors between cluster trajectories

See also

metric externalMetric

@@ -101,15 +100,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrend-package.html b/reference/latrend-package.html index c13c39a0..c16a9b88 100644 --- a/reference/latrend-package.html +++ b/reference/latrend-package.html @@ -3,7 +3,7 @@ - +
- +

latrend: A Framework for Clustering Longitudinal Data

- Source: R/zzz.R + Source: R/zzz.R
@@ -68,7 +68,7 @@

latrend: A Framework for Clustering Longitudinal Data

Features

- +

  • Unified cluster analysis, independent of the underlying algorithms used. Enabling users to compare the performance of various longitudinal cluster methods on the case study at hand.

  • Supports many different methods for longitudinal clustering out of the box (see the list of supported packages below).

    • @@ -81,7 +81,7 @@

    Features

Getting started

- +

The latrendData dataset is included with the package and is used in all examples. The plotTrajectories() function can be used to visualize any longitudinal dataset, given the id and time are specified.


@@ -115,7 +115,7 @@ 
Getting started

Vignettes

- +

Further step-by-step instructions on how to use the package are described in the vignettes.

Useful pages

- +

Data requirements and datasets: latrend-data latrendData PAP.adh

@@ -170,15 +170,15 @@

Author

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrend-parallel.html b/reference/latrend-parallel.html index 9b4c0749..c5b49c0f 100644 --- a/reference/latrend-parallel.html +++ b/reference/latrend-parallel.html @@ -4,7 +4,7 @@ - +
- +

Parallel computation using latrend

- Source: R/zzz.R + Source: R/zzz.R
@@ -70,7 +70,7 @@

Parallel computation using latrend

Windows

- +

On Windows, the parallel-package can be used to define parallel socket workers.


 nCores <- parallel::detectCores(logical = FALSE)
@@ -87,7 +87,7 @@ 
Windows

Unix

- +

On Unix systems, it is easier to setup parallelization as the R process is forked. In this example we use the doMC package:


@@ -127,15 +127,15 @@ 
Examples
-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrend.html b/reference/latrend.html index b1306691..b0996ac6 100644 --- a/reference/latrend.html +++ b/reference/latrend.html @@ -6,7 +6,7 @@ - +
- +

Cluster longitudinal data using the specified method

- Source: R/latrend.R + Source: R/latrend.R
@@ -83,25 +83,27 @@

Cluster longitudinal data using the specified method

Arguments

-
method
+ + +
method

An lcMethod object specifying the longitudinal cluster method to apply, or the name (as character) of the lcMethod subclass to instantiate.

-
data
+
data

The data of the trajectories to which to estimate the method for. Any inputs supported by trajectories() can be used, including data.frame and matrix.

-
...
+
...

Any other arguments to update the lcMethod definition with.

-
envir
+
envir

The environment in which to evaluate the method arguments via compose(). If the data argument is of type call then this environment is also used to evaluate the data argument.

-
verbose
+
verbose

The level of verbosity. Either an object of class Verbose (see R.utils::Verbose for details), a logical indicating whether to show basic computation information, a numeric indicating the verbosity level (see Verbose), @@ -110,9 +112,7 @@

Arguments

Value

- - -

A lcModel object representing the fitted solution.

+

A lcModel object representing the fitted solution.

Details

@@ -120,7 +120,7 @@

Details

See also

-

Other longitudinal cluster fit functions: +

Other longitudinal cluster fit functions: latrendBatch(), latrendBoot(), latrendCV(), @@ -150,15 +150,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrendBatch.html b/reference/latrendBatch.html index a2ff8db6..8bcea765 100644 --- a/reference/latrendBatch.html +++ b/reference/latrendBatch.html @@ -3,7 +3,7 @@ - +
- +

Cluster longitudinal data for a list of method specifications

- Source: R/latrend.R + Source: R/latrend.R
@@ -80,39 +80,41 @@

Cluster longitudinal data for a list of method specifications

Arguments

-
methods
+ + +
methods

A list of lcMethod objects.

-
data
+
data

The dataset(s) to which to fit the respective lcMethod on. Either a data.frame, matrix, list or an expression evaluating to one of the supported types. Multiple datasets can be supplied by encapsulating the datasets using data = .(df1, df2, ..., dfN). Doing this results in a more readable call associated with each fitted lcModel object.

-
cartesian
+
cartesian

Whether to fit the provided methods on each of the datasets. If cartesian=FALSE, only a single dataset may be provided or a list of data matching the length of methods.

-
seed
+
seed

Sets the seed for generating a seed number for the methods. Seeds are only set for methods without a seed argument or NULL seed.

-
parallel
+
parallel

Whether to enable parallel evaluation. See latrend-parallel. Method evaluation and dataset transformation is done on the calling thread.

-
errorHandling
+
errorHandling

Whether to "stop" on an error, or to "remove' evaluations that raised an error.

-
envir
+
envir

The environment in which to evaluate the lcMethod arguments.

-
verbose
+
verbose

The level of verbosity. Either an object of class Verbose (see R.utils::Verbose for details), a logical indicating whether to show basic computation information, a numeric indicating the verbosity level (see Verbose), @@ -121,9 +123,7 @@

Arguments

Value

- - -

A lcModels object. +

A lcModels object. In case of a model fit error under errorHandling = pass, a list is returned.

@@ -133,7 +133,7 @@

Details

See also

lcMethods

-

Other longitudinal cluster fit functions: +

Other longitudinal cluster fit functions: latrend(), latrendBoot(), latrendCV(), @@ -170,15 +170,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrendBoot.html b/reference/latrendBoot.html index 7eddde38..48262e51 100644 --- a/reference/latrendBoot.html +++ b/reference/latrendBoot.html @@ -3,7 +3,7 @@ - +
- +

Cluster longitudinal data using bootstrapping

- Source: R/latrend.R + Source: R/latrend.R
@@ -80,36 +80,38 @@

Cluster longitudinal data using bootstrapping

Arguments

-
method
+ + +
method

An lcMethod object specifying the longitudinal cluster method to apply, or the name (as character) of the lcMethod subclass to instantiate.

-
data
+
data

A data.frame.

-
samples
+
samples

The number of bootstrap samples to evaluate.

-
seed
+
seed

The seed to use. Optional.

-
parallel
+
parallel

Whether to enable parallel evaluation. See latrend-parallel. Method evaluation and dataset transformation is done on the calling thread.

-
errorHandling
+
errorHandling

Whether to "stop" on an error, or to "remove' evaluations that raised an error.

-
envir
+
envir

The environment in which to evaluate the method arguments via compose(). If the data argument is of type call then this environment is also used to evaluate the data argument.

-
verbose
+
verbose

The level of verbosity. Either an object of class Verbose (see R.utils::Verbose for details), a logical indicating whether to show basic computation information, a numeric indicating the verbosity level (see Verbose), @@ -118,18 +120,16 @@

Arguments

Value

- - -

A lcModels object of length samples.

+

A lcModels object of length samples.

See also

-

Other longitudinal cluster fit functions: +

Other longitudinal cluster fit functions: latrend(), latrendBatch(), latrendCV(), latrendRep()

-

Other validation methods: +

Other validation methods: createTestDataFold(), createTestDataFolds(), createTrainDataFolds(), @@ -162,15 +162,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrendCV.html b/reference/latrendCV.html index 16154dbe..9f227a94 100644 --- a/reference/latrendCV.html +++ b/reference/latrendCV.html @@ -4,7 +4,7 @@ - +
- +

Cluster longitudinal data over k folds

- Source: R/latrend.R + Source: R/latrend.R
@@ -82,36 +82,38 @@

Cluster longitudinal data over k folds

Arguments

-
method
+ + +
method

An lcMethod object specifying the longitudinal cluster method to apply, or the name (as character) of the lcMethod subclass to instantiate.

-
data
+
data

A data.frame.

-
folds
+
folds

The number of folds. Ten folds by default.

-
seed
+
seed

The seed to use. Optional.

-
parallel
+
parallel

Whether to enable parallel evaluation. See latrend-parallel. Method evaluation and dataset transformation is done on the calling thread.

-
errorHandling
+
errorHandling

Whether to "stop" on an error, or to "remove' evaluations that raised an error.

-
envir
+
envir

The environment in which to evaluate the method arguments via compose(). If the data argument is of type call then this environment is also used to evaluate the data argument.

-
verbose
+
verbose

The level of verbosity. Either an object of class Verbose (see R.utils::Verbose for details), a logical indicating whether to show basic computation information, a numeric indicating the verbosity level (see Verbose), @@ -120,18 +122,16 @@

Arguments

Value

- - -

A lcModels object of containing the folds training models.

+

A lcModels object of containing the folds training models.

See also

-

Other longitudinal cluster fit functions: +

Other longitudinal cluster fit functions: latrend(), latrendBatch(), latrendBoot(), latrendRep()

-

Other validation methods: +

Other validation methods: createTestDataFold(), createTestDataFolds(), createTrainDataFolds(), @@ -163,15 +163,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrendData-1.png b/reference/latrendData-1.png index 291bbf10..3a6697d7 100644 Binary files a/reference/latrendData-1.png and b/reference/latrendData-1.png differ diff --git a/reference/latrendData.html b/reference/latrendData.html index 870c783a..20e5f246 100644 --- a/reference/latrendData.html +++ b/reference/latrendData.html @@ -4,7 +4,7 @@ - +
- +

Artificial longitudinal dataset comprising three classes

- Source: R/data.R + Source: R/data.R
@@ -131,15 +131,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/latrendRep.html b/reference/latrendRep.html index e8b21acb..3839b975 100644 --- a/reference/latrendRep.html +++ b/reference/latrendRep.html @@ -3,7 +3,7 @@ - +
- +

Cluster longitudinal data repeatedly

- Source: R/latrend.R + Source: R/latrend.R
@@ -81,41 +81,43 @@

Cluster longitudinal data repeatedly

Arguments

-
method
+ + +
method

An lcMethod object specifying the longitudinal cluster method to apply, or the name (as character) of the lcMethod subclass to instantiate.

-
data
+
data

The data of the trajectories to which to estimate the method for. Any inputs supported by trajectories() can be used, including data.frame and matrix.

-
.rep
+
.rep

The number of repeated fits.

-
...
+
...

Any other arguments to update the lcMethod definition with.

-
.errorHandling
+
.errorHandling

Whether to "stop" on an error, or to "remove' evaluations that raised an error.

-
.seed
+
.seed

Set the seed for generating the respective seed for each of the repeated fits.

-
.parallel
+
.parallel

Whether to use parallel evaluation. See latrend-parallel.

-
envir
+
envir

The environment in which to evaluate the method arguments via compose(). If the data argument is of type call then this environment is also used to evaluate the data argument.

-
verbose
+
verbose

The level of verbosity. Either an object of class Verbose (see R.utils::Verbose for details), a logical indicating whether to show basic computation information, a numeric indicating the verbosity level (see Verbose), @@ -124,9 +126,7 @@

Arguments

Value

- - -

A lcModels object containing the resulting models.

+

A lcModels object containing the resulting models.

Details

@@ -134,7 +134,7 @@

Details

See also

-

Other longitudinal cluster fit functions: +

Other longitudinal cluster fit functions: latrend(), latrendBatch(), latrendBoot(), @@ -162,15 +162,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcApproxModel-class.html b/reference/lcApproxModel-class.html index f927a6c5..c2bc40d8 100644 --- a/reference/lcApproxModel-class.html +++ b/reference/lcApproxModel-class.html @@ -5,7 +5,7 @@ - +
- +

lcApproxModel class

- Source: R/modelApprox.R + Source: R/modelApprox.R
@@ -70,10 +70,10 @@

lcApproxModel class

- 
# S3 method for lcApproxModel
+    
# S3 method for class 'lcApproxModel'
 fitted(object, ..., clusters = trajectoryAssignments(object))
 
-# S4 method for lcApproxModel
+# S4 method for class 'lcApproxModel'
 predictForCluster(
   object,
   newdata,
@@ -86,31 +86,33 @@ 
lcApproxModel class

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
...

+
...

 
Additional arguments.

 
 
-
clusters

+
clusters

 
Optional cluster assignments per id. If unspecified, a matrix is returned containing the cluster-specific predictions per column.

 
 
-
newdata

+
newdata

 
A data.frame of trajectory data for which to compute trajectory assignments.

 
 
-
cluster

+
cluster

 
The cluster name (as character) to predict for.

 
 
-
what

+
what

 
The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

 
 
-
approxFun

+
approxFun

 
Function to interpolate between measurement moments, approx() by default.

 
 

@@ -127,15 +129,15 @@ 
Arguments

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.
- - + + diff --git a/reference/lcFitMethods.html b/reference/lcFitMethods.html index d19a2ad4..349090d7 100644 --- a/reference/lcFitMethods.html +++ b/reference/lcFitMethods.html @@ -11,7 +11,7 @@ - +
- +

Method fit modifiers

- Source: R/meta-fit.R, R/meta-fit-converged.R, R/meta-fit-rep.R + Source: R/meta-fit.R, R/meta-fit-converged.R, R/meta-fit-rep.R
@@ -90,23 +90,25 @@

Method fit modifiers

Arguments

-
method
+ + +
method

The lcMethod to use for fitting.

-
maxRep
+
maxRep

The maximum number of fit attempts

-
rep
+
rep

The number of fits

-
metric
+
metric

The internal metric to assess the fit.

-
maximize
+
maximize

Whether to maximize the metric. Otherwise, it is minimized.

@@ -189,15 +191,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMatrixMethod-class.html b/reference/lcMatrixMethod-class.html index 95e49f0e..4c8a634f 100644 --- a/reference/lcMatrixMethod-class.html +++ b/reference/lcMatrixMethod-class.html @@ -3,7 +3,7 @@ - +
- +

lcMatrixMethod

- Source: R/methodMatrix.R + Source: R/methodMatrix.R
@@ -66,35 +66,37 @@

lcMatrixMethod

- 
# S4 method for lcMatrixMethod
+    
# S4 method for class 'lcMatrixMethod'
 getName(object, ...)
 
-# S4 method for lcMatrixMethod
+# S4 method for class 'lcMatrixMethod'
 getShortName(object, ...)
 
-# S4 method for lcMatrixMethod
+# S4 method for class 'lcMatrixMethod'
 prepareData(method, data, verbose, ...)

Arguments

-
object
+ + +
object

The data or model or extract the trajectories from.

-
...
+
...

Arguments passed to trajectoryAssignments for generating the Cluster column.

-
method
+
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

@@ -111,15 +113,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethod-class.html b/reference/lcMethod-class.html index 1c63d00c..63c8ab3a 100644 --- a/reference/lcMethod-class.html +++ b/reference/lcMethod-class.html @@ -8,7 +8,7 @@ - +
- +

lcMethod class

- Source: R/method.R + Source: R/method.R
@@ -83,7 +83,7 @@

Details

Slots

- +
arguments

A list representing the arguments of the lcMethod object. @@ -101,7 +101,7 @@

Slots

Method arguments

- +

An lcMethod objects represent the specification of a method with a set of configurable parameters (referred to as arguments).

Arguments can be of any type. @@ -113,7 +113,7 @@

Method arguments

Implementation

- +

The base class lcMethod provides the logic for storing, evaluating, and printing the method parameters.

Subclasses of lcMethod differ only in the fitting procedure logic.

@@ -126,7 +126,7 @@

Implementation

See also

environment

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethodAkmedoids, @@ -146,7 +146,7 @@

See also

lcMethodMixtoolsNPRM, lcMethodRandom, lcMethodStratify

-

Other lcMethod functions: +

Other lcMethod functions: [[,lcMethod-method, as.data.frame.lcMethod(), as.data.frame.lcMethods(), @@ -207,15 +207,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethod-estimation.html b/reference/lcMethod-estimation.html index c3ba90e6..ce65b89f 100644 --- a/reference/lcMethod-estimation.html +++ b/reference/lcMethod-estimation.html @@ -4,7 +4,7 @@ - +
- +

Longitudinal cluster method (lcMethod) estimation procedure

- Source: R/method.R + Source: R/method.R
@@ -70,7 +70,7 @@

Longitudinal cluster method (lcMethod) estimation procedure

Estimation procedure

- +

The steps for estimating a lcMethod object are defined and executed as follows:

  1. compose(): Evaluate and finalize the method argument values.

  2. validate(): Check the validity of the method argument values in relation to the dataset.

    3. @@ -133,15 +133,15 @@

    Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodAkmedoids.html b/reference/lcMethodAkmedoids.html index f03f22cb..e1d0f580 100644 --- a/reference/lcMethodAkmedoids.html +++ b/reference/lcMethodAkmedoids.html @@ -3,7 +3,7 @@ - +
- +

Specify AKMedoids method

- Source: R/methodAKMedoids.R + Source: R/methodAKMedoids.R
@@ -79,44 +79,44 @@

Specify AKMedoids method

Arguments

-
response
+ + +
response

The name of the response variable.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identification variable.

-
nClusters
+
nClusters

The number of clusters to estimate.

-
clusterCenter
+
clusterCenter

A function for computing the cluster center representation.

-
crit
+
crit

Criterion to apply for internal model selection. Not applicable.

-
...
+
...

Arguments passed to akmedoids::akclustr. The following external arguments are ignored: traj, id_field, k

References

-

Adepeju M, Langton S, Bannister J (2020). -akmedoids: Anchored Kmedoids for Longitudinal Data Clustering. -R package version 0.1.5, https://CRAN.R-project.org/package=akmedoids.

+

adepeju2020akmedoidslatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -159,15 +159,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodCrimCV.html b/reference/lcMethodCrimCV.html index 59f13a83..94bf226c 100644 --- a/reference/lcMethodCrimCV.html +++ b/reference/lcMethodCrimCV.html @@ -3,7 +3,7 @@ - +
- +

Specify a zero-inflated repeated-measures GBTM method

- Source: R/methodCrimCV.R + Source: R/methodCrimCV.R
@@ -77,36 +77,36 @@

Specify a zero-inflated repeated-measures GBTM method

Arguments

-
response
+ + +
response

The name of the response variable.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters to estimate.

-
...
+
...

Arguments passed to crimCV::crimCV. The following external arguments are ignored: Dat, ng.

References

-

Nielsen JD (2018). -crimCV: Group-Based Modelling of Longitudinal Data. -R package version 0.9.6, https://CRAN.R-project.org/package=crimCV.

+

nielsen2018crimcvlatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -132,7 +132,7 @@

See also

Examples

# This example is not tested because crimCV sometimes fails
 # to converge and throws the error "object 'Frtr' not found"
-if (FALSE) {
+if (FALSE) { # \dontrun{
 data(latrendData)
 if (require("crimCV")) {
   method <- lcMethodCrimCV("Y", id = "Id", time = "Time", nClusters = 3, dpolyp = 1, init = 2)
@@ -147,7 +147,7 @@ 
Examples

     nClusters = 2, dpolyp = 1, init = 2)
   model <- latrend(method, data = TO1adj[1:100, ])
 }
-}
+} # }
@@ -162,15 +162,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodDtwclust.html b/reference/lcMethodDtwclust.html index f72e826b..87c4f00a 100644 --- a/reference/lcMethodDtwclust.html +++ b/reference/lcMethodDtwclust.html @@ -3,7 +3,7 @@ - +
- +

Specify time series clustering via dtwclust

- Source: R/methodDtwclust.R + Source: R/methodDtwclust.R
@@ -77,38 +77,36 @@

Specify time series clustering via dtwclust

Arguments

-
response
+ + +
response

The name of the response variable.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

Number of clusters.

-
...
+
...

Arguments passed to dtwclust::tsclust. The following arguments are ignored: series, k, trace.

References

-

Sardá-Espinosa A (2019). -“Time-Series Clustering in R Using the dtwclust Package.” -The R Journal. -doi:10.32614/RJ-2019-023 -.

+

sardaespinosa2019timelatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -157,11 +155,11 @@

Examples

#> #> Precomputing distance matrix... #> -#> Iteration 1: Changes / Distsum = 200 / 452.9951 -#> Iteration 2: Changes / Distsum = 12 / 407.0692 -#> Iteration 3: Changes / Distsum = 0 / 407.0692 +#> Iteration 1: Changes / Distsum = 200 / 399.6059 +#> Iteration 2: Changes / Distsum = 10 / 348.9926 +#> Iteration 3: Changes / Distsum = 0 / 348.9926 #> -#> Elapsed time is 0.133 seconds. +#> Elapsed time is 5.946 seconds. #>
@@ -177,15 +175,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodFeature.html b/reference/lcMethodFeature.html index a9756a89..08588c3b 100644 --- a/reference/lcMethodFeature.html +++ b/reference/lcMethodFeature.html @@ -3,7 +3,7 @@ - +
- +

Feature-based clustering

- Source: R/methodFeature.R + Source: R/methodFeature.R
@@ -80,42 +80,44 @@

Feature-based clustering

Arguments

-
response
+ + +
response

The name of the response variable.

-
representationStep
+
representationStep

A function with signature function(method, data) that computes the representation per strata, returned as a matrix. Alternatively, representationStep is a pre-computed representation matrix.

-
clusterStep
+
clusterStep

A function with signature function(repdata) that outputs a lcModel.

-
standardize
+
standardize

A function to standardize the output matrix of the representation step. By default, the output is shifted and rescaled to ensure zero mean and unit variance.

-
center
+
center

The function for computing the longitudinal cluster centers, used for representing the cluster trajectories.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identification variable.

-
...
+
...

Additional arguments.

Linear regresion & k-means example

- +

In this example we define a feature-based approach where each trajectory is represented using a linear regression model. The coefficients of the trajectories are then clustered using k-means.

@@ -164,7 +166,7 @@

Linear regresion & k-means example

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -198,15 +200,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodFlexmix.html b/reference/lcMethodFlexmix.html index fd09943b..f1370427 100644 --- a/reference/lcMethodFlexmix.html +++ b/reference/lcMethodFlexmix.html @@ -3,7 +3,7 @@ - +
- +

Method interface to flexmix()

- Source: R/methodFlexmix.R + Source: R/methodFlexmix.R
@@ -78,42 +78,40 @@

Method interface to flexmix()

Arguments

-
formula
+ + +
formula

A formula specifying the model.

-
formula.mb
+
formula.mb

A formula specifying the class membership model. By default, an intercept-only model is used.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters to estimate.

-
...
+
...

Arguments passed to flexmix::flexmix. The following arguments are ignored: data, concomitant, k.

References

-

Grün B, Leisch F (2008). -“FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters.” -Journal of Statistical Software, 28(4), 1--35. -doi:10.18637/jss.v028.i04 -.

+

gruen2008flexmixlatrend

See also

-

Other lcMethod package interfaces: +

Other lcMethod package interfaces: lcMethodFlexmixGBTM

@@ -139,15 +137,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodFlexmixGBTM.html b/reference/lcMethodFlexmixGBTM.html index 8f37bb9d..9c6301b8 100644 --- a/reference/lcMethodFlexmixGBTM.html +++ b/reference/lcMethodFlexmixGBTM.html @@ -3,7 +3,7 @@ - +
- +

Group-based trajectory modeling using flexmix

- Source: R/methodFlexmixGBTM.R + Source: R/methodFlexmixGBTM.R
@@ -78,42 +78,40 @@

Group-based trajectory modeling using flexmix

Arguments

-
formula
+ + +
formula

A formula specifying the model.

-
formula.mb
+
formula.mb

A formula specifying the class membership model. By default, an intercept-only model is used.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters to estimate.

-
...
+
...

Arguments passed to flexmix::flexmix or flexmix::FLXMRglm. The following arguments are ignored: data, k, trace.

References

-

Grün B, Leisch F (2008). -“FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters.” -Journal of Statistical Software, 28(4), 1--35. -doi:10.18637/jss.v028.i04 -.

+

gruen2008flexmixlatrend

See also

-

Other lcMethod package interfaces: +

Other lcMethod package interfaces: lcMethodFlexmix

@@ -138,15 +136,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodFunFEM.html b/reference/lcMethodFunFEM.html index 92dc231a..3fb7acc8 100644 --- a/reference/lcMethodFunFEM.html +++ b/reference/lcMethodFunFEM.html @@ -3,7 +3,7 @@ - +
- +

Specify a FunFEM method

- Source: R/methodFunFEM.R + Source: R/methodFunFEM.R
@@ -78,40 +78,40 @@

Specify a FunFEM method

Arguments

-
response
+ + +
response

The name of the response variable.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters to estimate.

-
basis
+
basis

The basis function. By default, a 3rd-order B-spline with 10 breaks is used.

-
...
+
...

Arguments passed to funFEM::funFEM. The following external arguments are ignored: fd, K, disp, graph.

References

-

Bouveyron C (2015). -funFEM: Clustering in the Discriminative Functional Subspace. -R package version 1.1, https://CRAN.R-project.org/package=funFEM.

+

bouveyron2015funfemlatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -181,15 +181,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodFunction.html b/reference/lcMethodFunction.html index 1945cbe3..7572289b 100644 --- a/reference/lcMethodFunction.html +++ b/reference/lcMethodFunction.html @@ -3,7 +3,7 @@ - +
- +

Specify a custom method based on a function

- Source: R/methodFunction.R + Source: R/methodFunction.R
@@ -78,33 +78,35 @@

Specify a custom method based on a function

Arguments

-
response
+ + +
response

The name of the response variable.

-
fun
+
fun

The cluster function with signature (method, data) that returns a lcModel object.

-
center
+
center

Optional function for computing the longitudinal cluster centers, with signature (x).

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identification variable.

-
name
+
name

The name of the method.

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -131,7 +133,7 @@

Examples

data(latrendData)
 # Stratification based on the mean response level
 clusfun <- function(data, response, id, time, ...) {
-  clusters <- data.table::as.data.table(data)[, mean(Y) > 0, by = Id]$V1
+  clusters <- data.table::as.data.table(data)[, mean(Y) > 0, by = Id]$V1
   lcModelPartition(
     data = data,
     trajectoryAssignments = factor(
@@ -160,15 +162,15 @@ 
Examples
-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodGCKM.html b/reference/lcMethodGCKM.html index 4be28a77..d4c8e25a 100644 --- a/reference/lcMethodGCKM.html +++ b/reference/lcMethodGCKM.html @@ -3,7 +3,7 @@ - +
- +

Two-step clustering through latent growth curve modeling and k-means

- Source: R/methodGCKM.R + Source: R/methodGCKM.R
@@ -79,39 +79,41 @@

Two-step clustering through latent growth curve modeling and k-means

Arguments

-
formula
+ + +
formula

Formula, including a random effects component for the trajectory. See lme4::lmer formula syntax.

-
time
+
time

The name of the time variable..

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters.

-
center
+
center

A function that computes the cluster center based on the original trajectories associated with the respective cluster. By default, the mean is computed.

-
standardize
+
standardize

A function to standardize the output matrix of the representation step. By default, the output is shifted and rescaled to ensure zero mean and unit variance.

-
...
+
...

Arguments passed to lme4::lmer. The following external arguments are ignored: data, centers, trace.

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -155,15 +157,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodKML.html b/reference/lcMethodKML.html index 04e0c65e..c524b2d4 100644 --- a/reference/lcMethodKML.html +++ b/reference/lcMethodKML.html @@ -3,7 +3,7 @@ - +
- +

Specify a longitudinal k-means (KML) method

- Source: R/methodKML.R + Source: R/methodKML.R
@@ -77,38 +77,36 @@

Specify a longitudinal k-means (KML) method

Arguments

-
response
+ + +
response

The name of the response variable.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters to estimate.

-
...
+
...

Arguments passed to kml::parALGO and kml::kml.

The following external arguments are ignored: object, nbClusters, parAlgo, toPlot, saveFreq

References

-

Genolini C, Alacoque X, Sentenac M, Arnaud C (2015). -“kml and kml3d: R Packages to Cluster Longitudinal Data.” -Journal of Statistical Software, 65(4), 1--34. -doi:10.18637/jss.v065.i04 -.

+

genolini2015kmllatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -134,7 +132,7 @@

See also

Examples

data(latrendData)
 
-if (require("kml")) {
+if (require("kml")) {
   method <- lcMethodKML("Y", id = "Id", time = "Time", nClusters = 3)
   model <- latrend(method, latrendData)
 }
@@ -154,15 +152,15 @@ 
Examples
-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodLMKM.html b/reference/lcMethodLMKM.html index 23970716..d8ecc0b0 100644 --- a/reference/lcMethodLMKM.html +++ b/reference/lcMethodLMKM.html @@ -3,7 +3,7 @@ - +
- +

Two-step clustering through linear regression modeling and k-means

- Source: R/methodLMKM.R + Source: R/methodLMKM.R
@@ -79,39 +79,41 @@

Two-step clustering through linear regression modeling and k-means

Arguments

-
formula
+ + +
formula

A formula specifying the linear trajectory model.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identification variable.

-
nClusters
+
nClusters

The number of clusters to estimate.

-
center
+
center

A function that computes the cluster center based on the original trajectories associated with the respective cluster. By default, the mean is computed.

-
standardize
+
standardize

A function to standardize the output matrix of the representation step. By default, the output is shifted and rescaled to ensure zero mean and unit variance.

-
...
+
...

Arguments passed to stats::lm. The following external arguments are ignored: x, data, control, centers, trace.

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -152,15 +154,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodLcmmGBTM.html b/reference/lcMethodLcmmGBTM.html index 40dcea6e..a8d4e2be 100644 --- a/reference/lcMethodLcmmGBTM.html +++ b/reference/lcMethodLcmmGBTM.html @@ -3,7 +3,7 @@ - +
- +

Specify GBTM method

- Source: R/methodLcmmGBTM.R + Source: R/methodLcmmGBTM.R
@@ -80,31 +80,33 @@

Specify GBTM method

Arguments

-
fixed
+ + +
fixed

The fixed effects formula.

-
mixture
+
mixture

The mixture-specific effects formula. See lcmm::hlme for details.

-
classmb
+
classmb

The cluster membership formula for the multinomial logistic model. See lcmm::hlme for details.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable. This replaces the subject argument of lcmm::hlme.

-
nClusters
+
nClusters

The number of clusters to fit. This replaces the ng argument of lcmm::hlme.

-
init
+
init

Alternative for the B argument of lcmm::hlme, for initializing the hlme fitting procedure. This is only applicable for nClusters > 1. Options:

  • "lme.random" (default): random initialization through a standard linear mixed model. Assigns a fitted standard linear mixed model enclosed in a call to random() to the B argument.

    • @@ -114,25 +116,19 @@

    Arguments

The argument is ignored if the B argument is specified, or nClusters = 1.

-
...
+
...

Arguments passed to lcmm::hlme. The following arguments are ignored: data, fixed, random, mixture, subject, classmb, returndata, ng, verbose, subset.

References

-

Proust-Lima C, Philipps V, Liquet B (2017). -“Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm.” -Journal of Statistical Software, 78(2), 1--56. -doi:10.18637/jss.v078.i02 -.

-

Proust-Lima C, Philipps V, Diakite A, Liquet B (2019). -lcmm: Extended Mixed Models Using Latent Classes and Latent Processes. -R package version: 1.8.1, https://cran.r-project.org/package=lcmm.

+

proustlima2017estimationlatrend

+

proustlima2019lcmmlatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -190,15 +186,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodLcmmGMM.html b/reference/lcMethodLcmmGMM.html index 8d813506..446ce7af 100644 --- a/reference/lcMethodLcmmGMM.html +++ b/reference/lcMethodLcmmGMM.html @@ -3,7 +3,7 @@ - +
- +

Specify GMM method using lcmm

- Source: R/methodLcmmGMM.R + Source: R/methodLcmmGMM.R
@@ -81,31 +81,33 @@

Specify GMM method using lcmm

Arguments

-
fixed
+ + +
fixed

The fixed effects formula.

-
mixture
+
mixture

The mixture-specific effects formula. See lcmm::hlme for details.

-
random
+
random

The random effects formula. See lcmm::hlme for details.

-
classmb
+
classmb

The cluster membership formula for the multinomial logistic model. See lcmm::hlme for details.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable. This replaces the subject argument of lcmm::hlme.

-
init
+
init

Alternative for the B argument of lcmm::hlme, for initializing the hlme fitting procedure. This is only applicable for nClusters > 1. Options:

  • "lme.random" (default): random initialization through a standard linear mixed model. Assigns a fitted standard linear mixed model enclosed in a call to random() to the B argument.

    • @@ -115,29 +117,23 @@

    Arguments

The argument is ignored if the B argument is specified, or nClusters = 1.

-
nClusters
+
nClusters

The number of clusters to fit. This replaces the ng argument of lcmm::hlme.

-
...
+
...

Arguments passed to lcmm::hlme. The following arguments are ignored: data, fixed, random, mixture, subject, classmb, returndata, ng, verbose, subset.

References

-

Proust-Lima C, Philipps V, Liquet B (2017). -“Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm.” -Journal of Statistical Software, 78(2), 1--56. -doi:10.18637/jss.v078.i02 -.

-

Proust-Lima C, Philipps V, Diakite A, Liquet B (2019). -lcmm: Extended Mixed Models Using Latent Classes and Latent Processes. -R package version: 1.8.1, https://cran.r-project.org/package=lcmm.

+

proustlima2017estimationlatrend

+

proustlima2019lcmmlatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -203,15 +199,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodMclustLLPA.html b/reference/lcMethodMclustLLPA.html index 77008bdd..faacc311 100644 --- a/reference/lcMethodMclustLLPA.html +++ b/reference/lcMethodMclustLLPA.html @@ -3,7 +3,7 @@ - +
- +

Longitudinal latent profile analysis

- Source: R/methodMclustLLPA.R + Source: R/methodMclustLLPA.R
@@ -77,36 +77,36 @@

Longitudinal latent profile analysis

Arguments

-
response
+ + +
response

The name of the response variable.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters to estimate.

-
...
+
...

Arguments passed to mclust::Mclust. The following external arguments are ignored: data, G, verbose.

References

-

Scrucca L, Fop M, Murphy TB, Raftery AE (2016). -“mclust 5: clustering, classification and density estimation using Gaussian finite mixture models.” -The R Journal, 8(1), 205--233.

+

scrucca2016mclustlatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -152,15 +152,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodMixAK_GLMM.html b/reference/lcMethodMixAK_GLMM.html index 828ce8e8..88101901 100644 --- a/reference/lcMethodMixAK_GLMM.html +++ b/reference/lcMethodMixAK_GLMM.html @@ -3,7 +3,7 @@ - +
- +

Specify a GLMM iwht a normal mixture in the random effects

- Source: R/methodMixAK_GLMM.R + Source: R/methodMixAK_GLMM.R
@@ -78,27 +78,29 @@

Specify a GLMM iwht a normal mixture in the random effects

Arguments

-
fixed
+ + +
fixed

A formula specifying the fixed effects of the model, including the response. Creates the y and x arguments for the call to mixAK::GLMM_MCMC.

-
random
+
random

A formula specifying the random effects of the model, including the random intercept. Creates the z and random.intercept arguments for the call to mixAK::GLMM_MCMC.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable. This is used to generate the id vector argument for the call to mixAK::GLMM_MCMC.

-
nClusters
+
nClusters

The number of clusters.

-
...
+
...

Arguments passed to mixAK::GLMM_MCMC. The following external arguments are ignored: y, x, z, random.intercept, silent.

@@ -109,15 +111,11 @@

Note

References

-

Komárek A (2009). -“A New R Package for Bayesian Estimation of Multivariate Normal Mixtures Allowing for Selection of the Number of Components and Interval-Censored Data.” -Computational Statistics and Data Analysis, 53(12), 3932--3947. -doi:10.1016/j.csda.2009.05.006 -.

+

komarek2009newlatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -167,13 +165,13 @@

Examples

#> #> Cluster sizes (K=3): #> A B C -#> 2 (17.9%) 77 (40.9%) 121 (41.2%) +#> 78 (23.5%) 1 (36.9%) 121 (39.7%) #> #> Number of obs: 2000, strata (Id): 200 #> #> Scaled residuals: #> Min. 1st Qu. Median Mean 3rd Qu. Max. -#> -3.1019 -0.7001 -0.2081 0.0000 0.7171 3.1983 +#> -2.1607 -0.7636 -0.4458 0.0000 1.0106 2.4587 #>
@@ -189,15 +187,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodMixTVEM.html b/reference/lcMethodMixTVEM.html index 216fce2e..1111b334 100644 --- a/reference/lcMethodMixTVEM.html +++ b/reference/lcMethodMixTVEM.html @@ -3,7 +3,7 @@ - +
- +

Specify a MixTVEM

- Source: R/methodMixTVEM.R + Source: R/methodMixTVEM.R
@@ -78,27 +78,29 @@

Specify a MixTVEM

Arguments

-
formula
+ + +
formula

A formula excluding the time component. Time-invariant covariates are detected automatically as these are a special case in MixTVEM.

-
formula.mb
+
formula.mb

A formula for cluster-membership prediction. Covariates must be time-invariant. Furthermore, the formula must contain an intercept.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters. This replaces the numClasses argument of the TVEMMixNormal function call.

-
...
+
...

Arguments passed to the TVEMMixNormal() function. The following optional arguments are ignored: doPlot, getSEs, numClasses.

@@ -110,10 +112,7 @@

Note

References

https://github.com/dziakj1/MixTVEM

-

Dziak JJ, Li R, Tan X, Shiffman S, Shiyko MP (2015). -“Modeling intensive longitudinal data with mixtures of nonparametric trajectories and time-varying effects.” -Psychological Methods, 20(4), 444--469. -ISSN 1939-1463.

+

dziak2015modelinglatrend

@@ -147,15 +146,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodMixtoolsGMM.html b/reference/lcMethodMixtoolsGMM.html index bea4f28d..8653092e 100644 --- a/reference/lcMethodMixtoolsGMM.html +++ b/reference/lcMethodMixtoolsGMM.html @@ -3,7 +3,7 @@ - +
- +

Specify mixed mixture regression model using mixtools

- Source: R/methodMixtoolsGMM.R + Source: R/methodMixtoolsGMM.R
@@ -77,38 +77,36 @@

Specify mixed mixture regression model using mixtools

Arguments

-
formula
+ + +
formula

Formula, including a random effects component for the trajectory. See lme4::lmer formula syntax.

-
time
+
time

The name of the time variable..

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters.

-
...
+
...

Arguments passed to mixtools::regmixEM.mixed. The following arguments are ignored: data, y, x, w, k, addintercept.fixed, verb.

References

-

Benaglia T, Chauveau D, Hunter DR, Young D (2009). -“mixtools: An R Package for Analyzing Finite Mixture Models.” -Journal of Statistical Software, 32(6), 1--29. -doi:10.18637/jss.v032.i06 -.

+

benaglia2009mixtoolslatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -166,15 +164,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodMixtoolsNPRM.html b/reference/lcMethodMixtoolsNPRM.html index dba3ec8a..d812cf33 100644 --- a/reference/lcMethodMixtoolsNPRM.html +++ b/reference/lcMethodMixtoolsNPRM.html @@ -3,7 +3,7 @@ - +
- +

Specify non-parametric estimation for independent repeated measures

- Source: R/methodMixtoolsNPRM.R + Source: R/methodMixtoolsNPRM.R
@@ -80,50 +80,48 @@

Specify non-parametric estimation for independent repeated measures

Arguments

-
response
+ + +
response

The name of the response variable.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identifier variable.

-
nClusters
+
nClusters

The number of clusters to estimate.

-
blockid
+
blockid

See mixtools::npEM.

-
bw
+
bw

See mixtools::npEM.

-
h
+
h

See mixtools::npEM.

-
...
+
...

Arguments passed to mixtools::npEM. The following optional arguments are ignored: data, x, mu0, verb.

References

-

Benaglia T, Chauveau D, Hunter DR, Young D (2009). -“mixtools: An R Package for Analyzing Finite Mixture Models.” -Journal of Statistical Software, 32(6), 1--29. -doi:10.18637/jss.v032.i06 -.

+

benaglia2009mixtoolslatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -167,15 +165,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodRandom.html b/reference/lcMethodRandom.html index bbd027db..6e47f95a 100644 --- a/reference/lcMethodRandom.html +++ b/reference/lcMethodRandom.html @@ -3,7 +3,7 @@ - +
- +

Specify a random-partitioning method

- Source: R/methodRandom.R + Source: R/methodRandom.R
@@ -80,47 +80,47 @@

Specify a random-partitioning method

Arguments

-
response
+ + +
response

The name of the response variable.

-
alpha
+
alpha

The Dirichlet parameters. Either scalar or of length nClusters. The higher alpha, the more uniform the clusters will be.

-
center
+
center

Optional function for computing the longitudinal cluster centers, with signature (x).

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identification variable.

-
nClusters
+
nClusters

The number of clusters.

-
name
+
name

The name of the method.

-
...
+
...

Additional arguments, such as the seed.

References

-

Frigyik BA, Kapila A, Gupta MR (2010). -“Introduction to the Dirichlet distribution and related processes.” -Technical Report UWEETR-2010-0006, Department of Electrical Engineering, University of Washington.

+

frigyik2010introductionlatrend

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -179,15 +179,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethodStratify.html b/reference/lcMethodStratify.html index 7aee0caf..0f25e19d 100644 --- a/reference/lcMethodStratify.html +++ b/reference/lcMethodStratify.html @@ -3,7 +3,7 @@ - +
- +

Specify a stratification method

- Source: R/methodStratify.R + Source: R/methodStratify.R
@@ -80,41 +80,43 @@

Specify a stratification method

Arguments

-
response
+ + +
response

The name of the response variable.

-
stratify
+
stratify

An expression returning a number or factor value per trajectory, representing the cluster assignment. Alternatively, a function can be provided that takes separate trajectory data.frame as input.

-
center
+
center

The function for computing the longitudinal cluster centers, used for representing the cluster trajectories.

-
nClusters
+
nClusters

The number of clusters. This is optional, as this can be derived from the largest assignment number by default, or the number of factor levels.

-
clusterNames
+
clusterNames

The names of the clusters. If a factor assignment is returned, the levels are used as the cluster names.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identification variable.

-
name
+
name

The name of the method.

See also

-

Other lcMethod implementations: +

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, @@ -206,15 +208,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcMethods.html b/reference/lcMethods.html index 709cde46..48313494 100644 --- a/reference/lcMethods.html +++ b/reference/lcMethods.html @@ -3,7 +3,7 @@ - +
- +

Generate a list of lcMethod objects

- Source: R/methods.R + Source: R/methods.R
@@ -71,11 +71,13 @@

Generate a list of lcMethod objects

Arguments

-
method
+ + +
method

The lcMethod to use as the template, which will be updated for each of the other arguments.

-
...
+
...

Any other arguments to update the lcMethod definition with. Values must be scalar, vector, list, or encapsulated in a .() call. Arguments wrapped in .() are passed as-is to the model call, ensuring a readable method. Arguments comprising a single symbol (e.g. a variable name) are interpreted as a constant. To force evaluation, specify arg=(var) or arg=force(var). @@ -84,15 +86,13 @@

Arguments

If a list is intended to be passed as a constant argument, then specifying arg=.(listObject) results in it being treated as such.

-
envir
+
envir

The environment in which to evaluate the method arguments.

Value

- - -

A list of lcMethod objects.

+

A list of lcMethod objects.

@@ -122,15 +122,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcModel-1.png b/reference/lcModel-1.png index 0d3ce254..e470830d 100644 Binary files a/reference/lcModel-1.png and b/reference/lcModel-1.png differ diff --git a/reference/lcModel-class.html b/reference/lcModel-class.html index f2cff828..22508deb 100644 --- a/reference/lcModel-class.html +++ b/reference/lcModel-class.html @@ -3,7 +3,7 @@ - +
- +

lcModel class

- Source: R/model.R + Source: R/model.R
@@ -68,11 +68,13 @@

lcModel class

Arguments

-
object
+ + +
object

The lcModel object.

-
...
+
...

Any additional arguments.

@@ -84,7 +86,7 @@

Details

Slots

- +
method

The lcMethod-class object specifying the arguments under which the model was fitted.

@@ -145,7 +147,7 @@

Slots

See also

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -194,15 +196,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcModel-data-filters.html b/reference/lcModel-data-filters.html index 4e9633c9..0bcad012 100644 --- a/reference/lcModel-data-filters.html +++ b/reference/lcModel-data-filters.html @@ -3,7 +3,7 @@ - +
- +

Data filters for lcModel

- Source: R/latrend.R + Source: R/latrend.R
@@ -75,47 +75,47 @@

Data filters for lcModel

Arguments

-
data
+ + +
data

The data.frame representing the model dataset.

-
id
+
id

The identifier variable name, see idVariable.

-
seed
+
seed

Optional seed for ensuring reproducibility.

-
fold
+
fold

The fold to select.

-
folds
+
folds

Total number of folds to create.

Value

- - -

A subset of data of type data.frame.

+

A subset of data of type data.frame.

See also

-

Other validation methods: +

Other validation methods: createTestDataFold(), createTestDataFolds(), createTrainDataFolds(), latrendBoot(), latrendCV()

-

Other validation methods: +

Other validation methods: createTestDataFold(), createTestDataFolds(), createTrainDataFolds(), latrendBoot(), latrendCV()

-

Other validation methods: +

Other validation methods: createTestDataFold(), createTestDataFolds(), createTrainDataFolds(), @@ -135,15 +135,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcModel-make.html b/reference/lcModel-make.html index b97d5818..0628575a 100644 --- a/reference/lcModel-make.html +++ b/reference/lcModel-make.html @@ -6,7 +6,7 @@ - +
- +

Cluster-handling functions for lcModel implementations.

- Source: R/make.R + Source: R/make.R
@@ -85,36 +85,32 @@

Cluster-handling functions for lcModel implementations.

Arguments

-
object
+ + +
object

The lcModel object.

-
clusters
+
clusters

The unprocessed trajectory cluster assignment vector.

-
n
+
n

The number of clusters.

-
clusterNames
+
clusterNames

Names of the clusters

-
sizes
+
sizes

Sizes of the respective clusters; the number of ids

Value

- - -

Factor cluster assignments.

- - +

Factor cluster assignments.

A cluster assignments index vector of type integer.

- -

A character vector length n with the cluster names.

@@ -142,15 +138,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcModel.html b/reference/lcModel.html index 32214905..6ad84230 100644 --- a/reference/lcModel.html +++ b/reference/lcModel.html @@ -8,7 +8,7 @@ - +
- +

Longitudinal cluster result (lcModel)

- Source: R/model.R + Source: R/model.R
@@ -78,7 +78,7 @@

Longitudinal cluster result (lcModel)

Functionality

- +

Clusters and partitioning:

  • nClusters(): The number of clusters this model represents.

  • clusterNames(): The names of the clusters.

    • @@ -141,15 +141,15 @@

    Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcModelPartition.html b/reference/lcModelPartition.html index fa87e31c..151f666d 100644 --- a/reference/lcModelPartition.html +++ b/reference/lcModelPartition.html @@ -4,7 +4,7 @@ - +
- +

Create a lcModel with pre-defined partitioning

- Source: R/modelPartition.R + Source: R/modelPartition.R
@@ -87,59 +87,61 @@

Create a lcModel with pre-defined partitioning

Arguments

-
data
+ + +
data

A data.frame representing the trajectory data.

-
response
+
response

The name of the response variable.

-
trajectoryAssignments
+
trajectoryAssignments

A vector of cluster membership per trajectory, a data.frame with an id column and "Cluster" column, or the name of the cluster membership column in the data argument.. For vector input, the type must be factor, character, or integer (1 to nClusters). The order of the trajectory, and thus the respective assignments, is determined by the id column of the data. Provide a factor id column for the input data to ensure that the ordering is as you aspect.

-
nClusters
+
nClusters

The number of clusters. Should be NA for trajectory assignments of type factor.

-
clusterNames
+
clusterNames

The names of the clusters, or a function with input n outputting a character vector of names. If unspecified, the names are determined from the trajectoryAssignments argument.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identification variable.

-
name
+
name

The name of the method.

-
center
+
center

The function for computing the longitudinal cluster centers, used for representing the cluster trajectories.

-
method
+
method

Optional lcMethod object that was used for fitting this model to the data.

-
converged
+
converged

Set the converged state.

-
model
+
model

An optional object to attach to the lcModelPartition object, representing the internal model that was used for obtaining the partition.

-
envir
+
envir

The environment associated with the model. Used for evaluating the assigned data object by model.data.lcModel.

@@ -175,15 +177,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcModelWeightedPartition.html b/reference/lcModelWeightedPartition.html index 755827a9..6ea949f6 100644 --- a/reference/lcModelWeightedPartition.html +++ b/reference/lcModelWeightedPartition.html @@ -3,7 +3,7 @@ - +
- +

Create a lcModel with pre-defined weighted partitioning

- Source: R/modelWeightedPartition.R + Source: R/modelWeightedPartition.R
@@ -79,31 +79,33 @@

Create a lcModel with pre-defined weighted partitioning

Arguments

-
data
+ + +
data

A data.frame representing the trajectory data.

-
response
+
response

The name of the response variable.

-
weights
+
weights

A numIds x numClusters matrix of partition probabilities.

-
clusterNames
+
clusterNames

The names of the clusters, or a function with input n outputting a character vector of names.

-
time
+
time

The name of the time variable.

-
id
+
id

The name of the trajectory identification variable.

-
name
+
name

The name of the method.

@@ -120,15 +122,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcModels-class.html b/reference/lcModels-class.html index 80bb1763..9b1fb2c1 100644 --- a/reference/lcModels-class.html +++ b/reference/lcModels-class.html @@ -6,7 +6,7 @@ - +
- +

lcModels: a list of lcModel objects

- Source: R/models.R + Source: R/models.R
@@ -74,7 +74,7 @@

lcModels: a list of lcModel objects

Functionality

- +

  • Print an argument summary for each of the models.

  • Convert to a data.frame of method arguments.

    • @@ -87,7 +87,7 @@

    Functionality

See also

-

Other lcModels functions: +

Other lcModels functions: as.lcModels(), lcModels, max.lcModels(), @@ -118,15 +118,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/lcModels.html b/reference/lcModels.html index c1d0733a..be07ebdd 100644 --- a/reference/lcModels.html +++ b/reference/lcModels.html @@ -4,7 +4,7 @@ - +
- +

Construct a list of lcModel objects

- Source: R/models.R + Source: R/models.R
@@ -73,19 +73,19 @@

Construct a list of lcModel objects

Arguments

-
...
+ + +
...

lcModel, lcModels, or a recursive list of lcModel objects. Arguments may be named.

Value

- - -

A lcModels object containing all specified lcModel objects.

+

A lcModels object containing all specified lcModel objects.

Functionality

- +

  • Print an argument summary for each of the models.

  • Convert to a data.frame of method arguments.

    • @@ -98,7 +98,7 @@

    Functionality

See also

-

Other lcModels functions: +

Other lcModels functions: as.lcModels(), lcModels-class, max.lcModels(), @@ -165,15 +165,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/logLik.lcModel.html b/reference/logLik.lcModel.html index 0e8e81b7..6a96d9c6 100644 --- a/reference/logLik.lcModel.html +++ b/reference/logLik.lcModel.html @@ -3,7 +3,7 @@ - +
- +

Extract the log-likelihood of a lcModel

- Source: R/model-evaluation.R + Source: R/model-evaluation.R
@@ -66,25 +66,25 @@

Extract the log-likelihood of a lcModel

- 
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 logLik(object, ...)

Arguments

-
object
+ + +
object

The lcModel object.

-
...
+
...

Additional arguments.

Value

- - -

A numeric with the computed log-likelihood. If unavailable, NA is returned.

+

A numeric with the computed log-likelihood. If unavailable, NA is returned.

Details

@@ -125,15 +125,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/match.call.all.html b/reference/match.call.all.html index b034f591..6da69ae1 100644 --- a/reference/match.call.all.html +++ b/reference/match.call.all.html @@ -3,7 +3,7 @@ - +
- +

Argument matching with defaults and parent ellipsis expansion

- Source: R/method.R + Source: R/method.R
@@ -71,17 +71,15 @@

Argument matching with defaults and parent ellipsis expansion

Arguments

-
n
+ + +
n

The number of frames to go back on the calling stack. See base::sys.parent for more details.

Value

- - -

A call

- - +

A call

See also

@@ -100,15 +98,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/max.lcModels.html b/reference/max.lcModels.html index afbddd11..118a0267 100644 --- a/reference/max.lcModels.html +++ b/reference/max.lcModels.html @@ -3,7 +3,7 @@ - +
- +

Select the lcModel with the highest metric value

- Source: R/models.R + Source: R/models.R
@@ -66,33 +66,33 @@

Select the lcModel with the highest metric value

- 
# S3 method for lcModels
+    
# S3 method for class 'lcModels'
 max(x, name, ...)

Arguments

-
x
+ + +
x

The lcModels object.

-
name
+
name

The name of the internal metric.

-
...
+
...

Additional arguments.

Value

- - -

The lcModel with the highest metric value

+

The lcModel with the highest metric value

Functionality

- +

  • Print an argument summary for each of the models.

  • Convert to a data.frame of method arguments.

    • @@ -106,7 +106,7 @@

    Functionality

    See also

    min.lcModels externalMetric

    -

    Other lcModels functions: +

    Other lcModels functions: as.lcModels(), lcModels, lcModels-class, @@ -151,7 +151,7 @@

    Examples

    #> #> Cluster sizes (K=2): #> A B -#> 120 (60%) 80 (40%) +#> 80 (40%) 120 (60%) #> #> Number of obs: 2000, strata (Id): 200 #> @@ -173,15 +173,15 @@

    Examples

    -

    Site built with pkgdown 2.0.9.

    +

    Site built with pkgdown 2.1.1.

    - - + + diff --git a/reference/meanNA.html b/reference/meanNA.html index c5216aaf..686466fd 100644 --- a/reference/meanNA.html +++ b/reference/meanNA.html @@ -3,7 +3,7 @@ - +
- +

Mean ignoring NAs

- Source: R/methodStratify.R + Source: R/methodStratify.R
@@ -71,14 +71,16 @@

Mean ignoring NAs

Arguments

-
x
+ + +
x

an R object. Currently there are methods for numeric/logical vectors and date, date-time and time interval objects. Complex vectors are allowed for trim = 0, only.

-
...
+
...

further arguments passed to or from other methods.

@@ -95,15 +97,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/metric.html b/reference/metric.html index f643220c..698db808 100644 --- a/reference/metric.html +++ b/reference/metric.html @@ -3,12 +3,9 @@ Note that there are many metrics available, and there exists no metric that works best in all scenarios. It is recommended to carefully consider which metric is most appropriate for your use case. Recommended overview papers: -Arbelaitz et al. (2013) - provide an extensive overview validity indices for cluster algorithms. -van der Nest et al. (2020) - provide an overview of metrics for mixture models (GBTM, GMM); primarily likelihood-based or posterior probability-based metrics. -Henson et al. (2007) - provide an overview of likelihood-based metrics for mixture models. +arbelaitz2013extensive;textuallatrend provide an extensive overview validity indices for cluster algorithms. +vandernest2020overview;textuallatrend provide an overview of metrics for mixture models (GBTM, GMM); primarily likelihood-based or posterior probability-based metrics. +henson2007detecting;textuallatrend provide an overview of likelihood-based metrics for mixture models. Call getInternalMetricNames() to retrieve the names of the defined internal metrics. @@ -16,7 +13,7 @@ - +
- +

Compute internal model metric(s)

- Source: R/generics.R, R/model.R, R/metricsInternal.R, and 1 more + Source: R/generics.R, R/model.R, R/metricsInternal.R, and 1 more
@@ -78,12 +75,9 @@

Compute internal model metric(s)

Compute one or more internal metrics for the given lcModel object.

Note that there are many metrics available, and there exists no metric that works best in all scenarios. It is recommended to carefully consider which metric is most appropriate for your use case.

-

Recommended overview papers:

  • Arbelaitz et al. (2013) - provide an extensive overview validity indices for cluster algorithms.

    • -

  • van der Nest et al. (2020) - provide an overview of metrics for mixture models (GBTM, GMM); primarily likelihood-based or posterior probability-based metrics.

    • -

  • Henson et al. (2007) - provide an overview of likelihood-based metrics for mixture models.

    • +

    Recommended overview papers:

    • arbelaitz2013extensive;textuallatrend provide an extensive overview validity indices for cluster algorithms.

      • +

    • vandernest2020overview;textuallatrend provide an overview of metrics for mixture models (GBTM, GMM); primarily likelihood-based or posterior probability-based metrics.

      • +

    • henson2007detecting;textuallatrend provide an overview of likelihood-based metrics for mixture models.

    Call getInternalMetricNames() to retrieve the names of the defined internal metrics.

    See the Details section below for a list of supported metrics.

@@ -91,122 +85,61 @@

Compute internal model metric(s)

metric(object, name = getOption("latrend.metric", c("WRSS", "APPA.mean")), ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 metric(object, name = getOption("latrend.metric", c("WRSS", "APPA.mean")), ...)
 
-# S4 method for list
+# S4 method for class 'list'
 metric(object, name, drop = TRUE)
 
-# S4 method for lcModels
+# S4 method for class 'lcModels'
 metric(object, name, drop = TRUE)

Arguments

-
object
+ + +
object

The lcModel, lcModels, or list of lcModel objects to compute the metrics for.

-
name
+
name

The name(s) of the metric(s) to compute. If no names are given, the names specified in the latrend.metric option (WRSS, APPA, AIC, BIC) are used.

-
...
+
...

Additional arguments.

-
drop
+
drop

Whether to return a numeric vector instead of a data.frame in case of a single metric.

Value

- - -

For metric(lcModel): A named numeric vector with the computed model metrics.

- - +

For metric(lcModel): A named numeric vector with the computed model metrics.

For metric(list): A data.frame with a metric per column.

- -

For metric(lcModels): A data.frame with a metric per column.

Supported internal metrics

- -
Metric nameDescriptionFunction / Reference
AICAkaike information criterion. A goodness-of-fit estimator that adjusts for model complexity (i.e., the number of parameters). Only available for models that support the computation of the model log-likelihood through logLik.stats::AIC(), (Akaike 1974)
APPA.meanMean of the average posterior probability of assignment (APPA) across clusters. A measure of the precision of the trajectory classifications. A score of 1 indicates perfect classification.APPA(), (Nagin 2005)
APPA.minLowest APPA among the clustersAPPA(), (Nagin 2005)
ASWAverage silhouette width based on the Euclidean distance(Rousseeuw 1987)
BICBayesian information criterion. A goodness-of-fit estimator that corrects for the degrees of freedom (i.e., the number of parameters) and sample size. Only available for models that support the computation of the model log-likelihood through logLik.stats::BIC(), (Schwarz 1978)
CAICConsistent Akaike information criterion(Bozdogan 1987)
CLCClassification likelihood criterion(McLachlan and Peel 2000)
convergedWhether the model converged during estimationconverged()
devianceThe model deviancestats::deviance()
DunnThe Dunn index(Dunn 1974)
entropyEntropy of the posterior probabilities
estimationTimeThe time needed for fitting the modelestimationTime()
EDEuclidean distance between the cluster trajectories and the assigned observed trajectories
ED.fitEuclidean distance between the cluster trajectories and the assigned fitted trajectories
ICL.BICIntegrated classification likelihood (ICL) approximated using the BIC(Biernacki et al. 2000)
logLikModel log-likelihoodstats::logLik()
MAEMean absolute error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
MahalanobisMahalanobis distance between the cluster trajectories and the assigned observed trajectories(Mahalanobis 1936)
MSEMean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
relativeEntropy, REA measure of the precision of the trajectory classification. A value of 1 indicates perfect classification, whereas a value of 0 indicates a non-informative uniform classification. It is the normalized version of entropy, scaled between [0, 1].(Ramaswamy et al. 1993) -, (Muthén 2004)
RMSERoot mean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
RSSResidual sum of squares under most likely cluster allocation
scaledEntropySee relativeEntropy
sigmaThe residual standard deviationstats::sigma()
ssBICSample-size adjusted BIC(Sclove 1987)
SEDStandardized Euclidean distance between the cluster trajectories and the assigned observed trajectories
SED.fitThe cluster-weighted standardized Euclidean distance between the cluster trajectories and the assigned fitted trajectories
WMAEMAE weighted by cluster-assignment probability
WMSEMSE weighted by cluster-assignment probability
WRMSERMSE weighted by cluster-assignment probability
WRSSRSS weighted by cluster-assignment probability
+ +
Metric nameDescriptionFunction / Reference
AICAkaike information criterion. A goodness-of-fit estimator that adjusts for model complexity (i.e., the number of parameters). Only available for models that support the computation of the model log-likelihood through logLik.stats::AIC(), akaike1974newlatrend
APPA.meanMean of the average posterior probability of assignment (APPA) across clusters. A measure of the precision of the trajectory classifications. A score of 1 indicates perfect classification.APPA(), nagin2005grouplatrend
APPA.minLowest APPA among the clustersAPPA(), nagin2005grouplatrend
ASWAverage silhouette width based on the Euclidean distancerousseeuw1987silhouetteslatrend
BICBayesian information criterion. A goodness-of-fit estimator that corrects for the degrees of freedom (i.e., the number of parameters) and sample size. Only available for models that support the computation of the model log-likelihood through logLik.stats::BIC(), schwarz1978estimatinglatrend
CAICConsistent Akaike information criterionbozdogan1987modellatrend
CLCClassification likelihood criterionmclachlan2000finitelatrend
convergedWhether the model converged during estimationconverged()
devianceThe model deviancestats::deviance()
DunnThe Dunn indexdunn1974welllatrend
entropyEntropy of the posterior probabilities
estimationTimeThe time needed for fitting the modelestimationTime()
EDEuclidean distance between the cluster trajectories and the assigned observed trajectories
ED.fitEuclidean distance between the cluster trajectories and the assigned fitted trajectories
ICL.BICIntegrated classification likelihood (ICL) approximated using the BICbiernacki2000assessinglatrend
logLikModel log-likelihoodstats::logLik()
MAEMean absolute error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
MahalanobisMahalanobis distance between the cluster trajectories and the assigned observed trajectoriesmahalanobis1936generalizedlatrend
MSEMean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
relativeEntropy, REA measure of the precision of the trajectory classification. A value of 1 indicates perfect classification, whereas a value of 0 indicates a non-informative uniform classification. It is the normalized version of entropy, scaled between [0, 1].ramaswamy1993empiricallatrend, muthen2004latentlatrend
RMSERoot mean squared error of the fitted trajectories (assigned to the most likely respective cluster) to the observed trajectories
RSSResidual sum of squares under most likely cluster allocation
scaledEntropySee relativeEntropy
sigmaThe residual standard deviationstats::sigma()
ssBICSample-size adjusted BICsclove1987applicationlatrend
SEDStandardized Euclidean distance between the cluster trajectories and the assigned observed trajectories
SED.fitThe cluster-weighted standardized Euclidean distance between the cluster trajectories and the assigned fitted trajectories
WMAEMAE weighted by cluster-assignment probability
WMSEMSE weighted by cluster-assignment probability
WRMSERMSE weighted by cluster-assignment probability
WRSSRSS weighted by cluster-assignment probability

Implementation

- +

See the documentation of the defineInternalMetric() function for details on how to define your own metrics.

References

-

Akaike H (1974). -“A new look at the statistical model identification.” -IEEE Transactions on Automatic Control, 19(6), 716-723. -doi:10.1109/TAC.1974.1100705 -.

Arbelaitz O, Gurrutxaga I, Muguerza J, Pérez JM, Perona I (2013). -“An extensive comparative study of cluster validity indices.” -Pattern recognition, 46(1), 243--256. -ISSN 0031-3203, doi:10.1016/j.patcog.2012.07.021 -.

Biernacki C, Celeux G, Govaert G (2000). -“Assessing a mixture model for clustering with the integrated completed likelihood.” -IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7), 719-725. -doi:10.1109/34.865189 -.

Bozdogan H (1987). -“Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions.” -Psychometrika, 52, 345--370. -doi:10.1007/BF02294361 -.

Dunn JC (1974). -“Well-Separated Clusters and Optimal Fuzzy Partitions.” -Journal of Cybernetics, 4(1), 95-104. -doi:10.1080/01969727408546059 -.

Henson JM, Reise SP, Kim KH (2007). -“Detecting Mixtures From Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics.” -Structural Equation Modeling: A Multidisciplinary Journal, 14(2), 202--226. -doi:10.1080/10705510709336744 -.

Mahalanobis PC (1936). -“On the generalized distance in statistics.” -Proceedings of the National Institute of Sciences (Calcutta), 2(1), 49--55.

McLachlan G, Peel D (2000). -Finite Mixture Models. -John Wiley & Sons, Inc. -ISBN 9780471006268.

Muthén B (2004). -“Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data.” -In The SAGE Handbook of Quantitative Methodology for the Social Sciences, 346--369. -SAGE Publications, Inc. -doi:10.4135/9781412986311.n19 -.

Nagin DS (2005). -Group-based modeling of development. -Harvard University Press. -ISBN 9780674041318, doi:10.4159/9780674041318 -.

Ramaswamy V, Desarbo W, Reibstein D, Robinson W (1993). -“An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data.” -Marketing Science, 12(1), 103-124. -doi:10.1287/mksc.12.1.103 -.

Rousseeuw PJ (1987). -“Silhouettes: A graphical aid to the interpretation and validation of cluster analysis.” -Journal of Computational and Applied Mathematics, 20, 53-65. -ISSN 0377-0427, doi:10.1016/0377-0427(87)90125-7 -.

Schwarz G (1978). -“Estimating the Dimension of a Model.” -The Annals of Statistics, 6(2), 461 -- 464.

Sclove SL (1987). -“Application of model-selection criteria to some problems in multivariate analysis.” -Psychometrika, 52(3), 333--343. -doi:10.1007/BF02294360 -.

van der Nest G, Lima Passos V, Candel MJ, van Breukelen GJ (2020). -“An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software.” -Advances in Life Course Research, 43, 100323. -ISSN 1040-2608, doi:10.1016/j.alcr.2019.100323 -.

+

See also

externalMetric min.lcModels max.lcModels

-

Other metric functions: +

Other metric functions: defineExternalMetric(), defineInternalMetric(), externalMetric(), @@ -214,7 +147,7 @@

See also

getExternalMetricNames(), getInternalMetricDefinition(), getInternalMetricNames()

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -279,15 +212,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/min.lcModels.html b/reference/min.lcModels.html index 9e54f20a..96dfe8bf 100644 --- a/reference/min.lcModels.html +++ b/reference/min.lcModels.html @@ -3,7 +3,7 @@ - +
- +

Select the lcModel with the lowest metric value

- Source: R/models.R + Source: R/models.R
@@ -66,33 +66,33 @@

Select the lcModel with the lowest metric value

- 
# S3 method for lcModels
+    
# S3 method for class 'lcModels'
 min(x, name, ...)

Arguments

-
x
+ + +
x

The lcModels object

-
name
+
name

The name of the internal metric.

-
...
+
...

Additional arguments.

Value

- - -

The lcModel with the lowest metric value

+

The lcModel with the lowest metric value

Functionality

- +

  • Print an argument summary for each of the models.

  • Convert to a data.frame of method arguments.

    • @@ -106,7 +106,7 @@

    Functionality

    See also

    max.lcModels externalMetric

    -

    Other lcModels functions: +

    Other lcModels functions: as.lcModels(), lcModels, lcModels-class, @@ -170,15 +170,15 @@

    Examples

    -

    Site built with pkgdown 2.0.9.

    +

    Site built with pkgdown 2.1.1.

    - - + + diff --git a/reference/model.data.html b/reference/model.data.html index dcf6ebc5..88bac259 100644 --- a/reference/model.data.html +++ b/reference/model.data.html @@ -3,7 +3,7 @@ - +
- +

Extract the model training data

- Source: R/model.R + Source: R/model.R
@@ -71,11 +71,13 @@

Extract the model training data

Arguments

-
object
+ + +
object

The object.

-
...
+
...

Additional arguments.

@@ -92,15 +94,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/model.data.lcModel.html b/reference/model.data.lcModel.html index 18cd882c..1e106954 100644 --- a/reference/model.data.lcModel.html +++ b/reference/model.data.lcModel.html @@ -3,7 +3,7 @@ - +
- +

Extract the model data that was used for fitting

- Source: R/model.R + Source: R/model.R
@@ -66,25 +66,25 @@

Extract the model data that was used for fitting

- 
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 model.data(object, ...)

Arguments

-
object
+ + +
object

The lcModel object.

-
...
+
...

Additional arguments.

Value

- - -

The full data.frame that was used for fitting the lcModel.

+

The full data.frame that was used for fitting the lcModel.

See also

@@ -2112,15 +2112,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/model.frame.lcModel.html b/reference/model.frame.lcModel.html index 78907c06..6b1a5fe8 100644 --- a/reference/model.frame.lcModel.html +++ b/reference/model.frame.lcModel.html @@ -3,7 +3,7 @@ - +
- +

Extract model training data

- Source: R/model.R + Source: R/model.R
@@ -66,30 +66,30 @@

Extract model training data

- 
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 model.frame(formula, ...)

Arguments

-
formula
+ + +
formula

The lcModel object.

-
...
+
...

Additional arguments.

Value

- - -

A data.frame containing the variables used by the model.

+

A data.frame containing the variables used by the model.

See also

stats::model.frame model.data.lcModel

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -2147,15 +2147,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/nClusters.html b/reference/nClusters.html index 20a42964..2196c648 100644 --- a/reference/nClusters.html +++ b/reference/nClusters.html @@ -3,7 +3,7 @@ - +
- +

Number of clusters

- Source: R/generics.R, R/model.R + Source: R/generics.R, R/model.R
@@ -68,30 +68,30 @@

Number of clusters

nClusters(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 nClusters(object, ...)

Arguments

-
object
+ + +
object

The object

-
...
+
...

Not used.

Value

- - -

The number of clusters: a scalar numeric non-zero count.

+

The number of clusters: a scalar numeric non-zero count.

See also

nIds nobs

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -149,15 +149,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/nIds.html b/reference/nIds.html index 0762ce7f..c0de8c5e 100644 --- a/reference/nIds.html +++ b/reference/nIds.html @@ -4,7 +4,7 @@ - +
- +

Number of trajectories

- Source: R/model.R + Source: R/model.R
@@ -73,20 +73,20 @@

Number of trajectories

Arguments

-
object
+ + +
object

The lcModel object.

Value

- - -

An integer with the number of trajectories on which the lcModel was fitted.

+

An integer with the number of trajectories on which the lcModel was fitted.

See also

nobs nClusters

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -144,15 +144,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/names-lcMethod-method.html b/reference/names-lcMethod-method.html index 6ffd96be..96d616f0 100644 --- a/reference/names-lcMethod-method.html +++ b/reference/names-lcMethod-method.html @@ -3,7 +3,7 @@ - +
- +

lcMethod argument names

- Source: R/method.R + Source: R/method.R
@@ -66,31 +66,29 @@

lcMethod argument names

- 
# S4 method for lcMethod
+    
# S4 method for class 'lcMethod'
 length(x)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 names(x)

Arguments

-
x
+ + +
x

The lcMethod object.

Value

- - -

The number of arguments, as scalar integer.

- - +

The number of arguments, as scalar integer.

A character vector of argument names.

See also

-

Other lcMethod functions: +

Other lcMethod functions: [[,lcMethod-method, as.data.frame.lcMethod(), as.data.frame.lcMethods(), @@ -125,15 +123,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/nobs.lcModel.html b/reference/nobs.lcModel.html index 9f1ea4f4..05a97108 100644 --- a/reference/nobs.lcModel.html +++ b/reference/nobs.lcModel.html @@ -4,7 +4,7 @@ - +
- +

Number of observations used for the lcModel fit

- Source: R/model.R + Source: R/model.R
@@ -68,24 +68,26 @@

Number of observations used for the lcModel fit

- 
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 nobs(object, ...)

Arguments

-
object
+ + +
object

The lcModel object.

-
...
+
...

Additional arguments.

See also

nIds nClusters

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -143,15 +145,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/plot-lcModel-method-1.png b/reference/plot-lcModel-method-1.png index da201172..5f0b27a2 100644 Binary files a/reference/plot-lcModel-method-1.png and b/reference/plot-lcModel-method-1.png differ diff --git a/reference/plot-lcModel-method.html b/reference/plot-lcModel-method.html index 0a30bd22..af7d1386 100644 --- a/reference/plot-lcModel-method.html +++ b/reference/plot-lcModel-method.html @@ -4,7 +4,7 @@ - +
- +

Plot a lcModel

- Source: R/model.R + Source: R/model.R
@@ -68,38 +68,38 @@

Plot a lcModel

- 
# S4 method for lcModel,ANY
+    
# S4 method for class 'lcModel,ANY'
 plot(x, y, ...)

Arguments

-
x
+ + +
x

The lcModel object.

-
y
+
y

Not used.

-
...
+
...

Arguments passed on to plotClusterTrajectories

object

The (cluster) trajectory data.

- +

Value

- - -

A ggplot object.

+

A ggplot object.

See also

plotClusterTrajectories plotFittedTrajectories plotTrajectories ggplot2::ggplot

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -160,15 +160,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/plot-lcModels-method.html b/reference/plot-lcModels-method.html index 7feefec2..d52239f3 100644 --- a/reference/plot-lcModels-method.html +++ b/reference/plot-lcModels-method.html @@ -3,7 +3,7 @@ - +
- +

Grid plot for a list of models

- Source: R/models.R + Source: R/models.R
@@ -66,30 +66,32 @@

Grid plot for a list of models

- 
# S4 method for lcModels,ANY
+    
# S4 method for class 'lcModels,ANY'
 plot(x, y, ..., subset, gridArgs = list())

Arguments

-
x
+ + +
x

The lcModels object.

-
y
+
y

Not used.

-
...
+
...

Additional parameters passed to the plot() call for each lcModel object.

-
subset
+
subset

Logical expression based on the lcModel method arguments, indicating which lcModel objects to keep.

-
gridArgs
+
gridArgs

Named list of parameters passed to gridExtra::arrangeGrob.

@@ -106,15 +108,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/plotClusterTrajectories-1.png b/reference/plotClusterTrajectories-1.png index 2bb096a8..8648112e 100644 Binary files a/reference/plotClusterTrajectories-1.png and b/reference/plotClusterTrajectories-1.png differ diff --git a/reference/plotClusterTrajectories.html b/reference/plotClusterTrajectories.html index d770d374..17b9a8df 100644 --- a/reference/plotClusterTrajectories.html +++ b/reference/plotClusterTrajectories.html @@ -3,7 +3,7 @@ - +
- +

Plot cluster trajectories

- Source: R/generics.R, R/trajectories.R, R/model.R + Source: R/generics.R, R/trajectories.R, R/model.R
@@ -68,7 +68,7 @@

Plot cluster trajectories

plotClusterTrajectories(object, ...)
 
-# S4 method for data.frame
+# S4 method for class 'data.frame'
 plotClusterTrajectories(
   object,
   response,
@@ -83,7 +83,7 @@ 
Plot cluster trajectories

   ...
 )
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 plotClusterTrajectories(
   object,
   what = "mu",
@@ -98,42 +98,44 @@ 
Plot cluster trajectories

 
     

     
Arguments

-    
object

+
+
+
object

 
The (cluster) trajectory data.

 
 
-
...

+
...

 
Additional arguments passed to clusterTrajectories.

 
 
-
response

+
response

 
The response variable name, see responseVariable.

 
 
-
cluster

+
cluster

 
The cluster assignment column

 
 
-
clusterOrder

+
clusterOrder

 
Specify which clusters to plot and the order.
 Can be the cluster names or index.
 By default, all clusters are shown.

 
 
-
clusterLabeler

+
clusterLabeler

 
A function(clusterNames, clusterSizes) that generates plot labels for the clusters.
 By default the cluster name with the proportional size is shown, see make.clusterPropLabels.

 
 
-
time

+
time

 
The time variable name, see timeVariable.

 
 
-
center

+
center

 
A function for aggregating multiple points at the same point in time

 
 
-
trajectories

+
trajectories

 
Whether to additionally plot the original trajectories (TRUE),
 or to show the expected interval (standard deviation, standard error, range, or percentile range)
 of the observations at the respective moment in time.

@@ -142,33 +144,31 @@ 
Arguments

 By default (FALSE), no information on the underlying trajectories is shown.

 
 
-
facet

+
facet

 
Whether to facet by cluster. This is done by default when trajectories is enabled.

 
 
-
id

+
id

 
Id column. Only needed when trajectories = TRUE.

 
 
-
what

+
what

 
The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

 
 
-
at

+
at

 
A numeric vector of the times at which to compute the cluster trajectories.

 
 

     

     
Value

-    
-
-
A ggplot object.

+    
A ggplot object.

     

     

     
See also

     
clusterTrajectories

 
plotTrajectories plot

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -256,15 +256,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/plotFittedTrajectories-1.png b/reference/plotFittedTrajectories-1.png
index 7ec79a6f..6a51aaa5 100644
Binary files a/reference/plotFittedTrajectories-1.png and b/reference/plotFittedTrajectories-1.png differ
diff --git a/reference/plotFittedTrajectories.html b/reference/plotFittedTrajectories.html
index 8e9217c4..a029ade7 100644
--- a/reference/plotFittedTrajectories.html
+++ b/reference/plotFittedTrajectories.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
- +

Plot the fitted trajectories

- Source: R/generics.R, R/model.R + Source: R/generics.R, R/model.R
@@ -68,31 +68,31 @@

Plot the fitted trajectories

plotFittedTrajectories(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 plotFittedTrajectories(object, ...)

Arguments

-
object
+ + +
object

The model.

-
...
+
...

Arguments passed to fittedTrajectories() and plotTrajectories.

Value

- - -

A ggplot object.

+

A ggplot object.

See also

fittedTrajectories

plotClusterTrajectories plotTrajectories plot

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -153,15 +153,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/plotMetric-1.png b/reference/plotMetric-1.png index e948a5eb..c0fd8c94 100644 Binary files a/reference/plotMetric-1.png and b/reference/plotMetric-1.png differ diff --git a/reference/plotMetric-2.png b/reference/plotMetric-2.png index c19c447b..c1dded75 100644 Binary files a/reference/plotMetric-2.png and b/reference/plotMetric-2.png differ diff --git a/reference/plotMetric.html b/reference/plotMetric.html index ed8d29f7..3aa970f2 100644 --- a/reference/plotMetric.html +++ b/reference/plotMetric.html @@ -3,7 +3,7 @@ - +
- +

Plot one or more internal metrics for all lcModels

- Source: R/models.R + Source: R/models.R
@@ -71,24 +71,26 @@

Plot one or more internal metrics for all lcModels

Arguments

-
models
+ + +
models

A lcModels or list of lcModel objects to compute and plot the metrics of.

-
name
+
name

The name(s) of the metric(s) to compute. If no names are given, the names specified in the latrend.metric option (WRSS, APPA, AIC, BIC) are used.

-
by
+
by

The argument name along which methods are plotted.

-
subset
+
subset

Logical expression based on the lcModel method arguments, indicating which lcModel objects to keep.

-
group
+
group

The argument names to use for determining groups of different models. By default, all arguments are included. Specifying group = character() disables grouping. @@ -98,13 +100,11 @@

Arguments

Value

- - -

ggplot2 object.

+

ggplot2 object.

Functionality

- +

  • Print an argument summary for each of the models.

  • Convert to a data.frame of method arguments.

    • @@ -117,7 +117,7 @@

    Functionality

See also

-

Other lcModels functions: +

Other lcModels functions: as.lcModels(), lcModels, lcModels-class, @@ -159,15 +159,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/plotTrajectories-1.png b/reference/plotTrajectories-1.png index 291bbf10..3a6697d7 100644 Binary files a/reference/plotTrajectories-1.png and b/reference/plotTrajectories-1.png differ diff --git a/reference/plotTrajectories-2.png b/reference/plotTrajectories-2.png index 27743081..88c9b937 100644 Binary files a/reference/plotTrajectories-2.png and b/reference/plotTrajectories-2.png differ diff --git a/reference/plotTrajectories.html b/reference/plotTrajectories.html index 17220c85..41138ff7 100644 --- a/reference/plotTrajectories.html +++ b/reference/plotTrajectories.html @@ -3,7 +3,7 @@ - +
- +

Plot the data trajectories

- Source: R/generics.R, R/trajectories.R, R/model.R + Source: R/generics.R, R/trajectories.R, R/model.R
@@ -68,7 +68,7 @@

Plot the data trajectories

plotTrajectories(object, ...)
 
-# S4 method for data.frame
+# S4 method for class 'data.frame'
 plotTrajectories(
   object,
   response,
@@ -79,42 +79,44 @@ 
Plot the data trajectories

   ...
 )
 
-# S4 method for ANY
+# S4 method for class 'ANY'
 plotTrajectories(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 plotTrajectories(object, ...)

Arguments

-
object
+ + +
object

The data or model or extract the trajectories from.

-
...
+
...

Additional arguments passed to trajectories.

-
response
+
response

Response variable character name or a call.

-
cluster
+
cluster

Whether to plot trajectories grouped by cluster (determined by the "Cluster" column). Alternatively, the name of the cluster column indicating trajectory cluster membership. If unspecified, trajectories are grouped if the object contains a "Cluster" column.

-
time
+
time

The time variable name, see timeVariable.

-
id
+
id

The identifier variable name, see idVariable.

-
facet
+
facet

Whether to facet by cluster.

@@ -170,15 +172,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/postFit.html b/reference/postFit.html index cf7a9ed1..a24bcda3 100644 --- a/reference/postFit.html +++ b/reference/postFit.html @@ -12,7 +12,7 @@ - +
- +

lcMethod estimation step: logic for post-processing the fitted lcModel

- Source: R/generics.R, R/method.R + Source: R/generics.R, R/method.R
@@ -85,45 +85,45 @@

lcMethod estimation step: logic for post-processing the fitted 
postFit(method, data, model, envir, verbose, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 postFit(method, data, model, envir, verbose)

Arguments

-
method
+ + +
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
model
+
model

The lcModel object returned by fit().

-
envir
+
envir

The environment containing variables generated by prepareData() and preFit().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
...
+
...

Not used.

Value

- - -

The updated lcModel object.

+

The updated lcModel object.

Implementation

- +

The method is intended to be able to be called on previously fitted lcModel objects as well, allowing for potential bugfixes or additions to previously fitted models. Therefore, when implementing this method, ensure that you do not discard information from the model which would prevent the method from being run a second time on the object.

@@ -137,7 +137,7 @@

Implementation

Estimation procedure

- +

The steps for estimating a lcMethod object are defined and executed as follows:

  1. compose(): Evaluate and finalize the method argument values.

  2. validate(): Check the validity of the method argument values in relation to the dataset.

    3. @@ -160,15 +160,15 @@

    Estimation procedure

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/postProbFromObs.html b/reference/postProbFromObs.html index 84d9dbf4..61b04006 100644 --- a/reference/postProbFromObs.html +++ b/reference/postProbFromObs.html @@ -3,7 +3,7 @@ - +
- +

Compute the id-specific postprob matrix from a given observation-level postprob matrix

- Source: R/compute.R + Source: R/compute.R
@@ -71,11 +71,13 @@

Compute the id-specific postprob matrix from a given observation-level postp

Arguments

-
mat
+ + +
mat

A posterior probability matrix at the observation level.

-
rowIds
+
rowIds

The row trajectory identifier integer vector.

@@ -92,15 +94,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/postprob.html b/reference/postprob.html index 7df616f1..335875ae 100644 --- a/reference/postprob.html +++ b/reference/postprob.html @@ -3,7 +3,7 @@ - +
- +

Posterior probability per fitted trajectory

- Source: R/generics.R, R/model.R + Source: R/generics.R, R/model.R
@@ -68,25 +68,25 @@

Posterior probability per fitted trajectory

postprob(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 postprob(object, ...)

Arguments

-
object
+ + +
object

The model.

-
...
+
...

Not used.

Value

- - -

An I-by-K numeric matrix with I = nIds(object) and K = nClusters(object).

+

An I-by-K numeric matrix with I = nIds(object) and K = nClusters(object).

Details

@@ -94,7 +94,7 @@

Details

Implementation

- +

Classes extending lcModel should override this method.


 setMethod("postprob", "lcModelExt", function(object, ...) {
@@ -104,7 +104,7 @@ 
Implementation

Troubleshooting

- +

If you are getting errors about undefined model signatures when calling postprob(model), check whether the postprob() function is still the one defined by the latrend package. @@ -113,7 +113,7 @@

Troubleshooting

See also

trajectoryAssignments predictPostprob predictAssignments

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -586,15 +586,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/postprobFromAssignments.html b/reference/postprobFromAssignments.html index fa16c324..1141730e 100644 --- a/reference/postprobFromAssignments.html +++ b/reference/postprobFromAssignments.html @@ -3,7 +3,7 @@ - +
- +

Create a posterior probability matrix from a vector of cluster assignments.

- Source: R/methodStratify.R + Source: R/methodStratify.R
@@ -71,11 +71,13 @@

Create a posterior probability matrix from a vector of cluster assignments.<

Arguments

-
assignments
+ + +
assignments

Integer vector indicating cluster assignment per trajectory

-
k
+
k

The number of clusters.

@@ -92,15 +94,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/preFit.html b/reference/preFit.html index 2705d138..915ef824 100644 --- a/reference/preFit.html +++ b/reference/preFit.html @@ -6,7 +6,7 @@ - +
- +

lcMethod estimation step: method preparation logic

- Source: R/generics.R, R/method.R + Source: R/generics.R, R/method.R
@@ -74,41 +74,41 @@

lcMethod estimation step: method preparation logic

preFit(method, data, envir, verbose, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 preFit(method, data, envir, verbose)

Arguments

-
method
+ + +
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment containing additional data variables returned by prepareData().

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
...
+
...

Not used.

Value

- - -

The updated environment that will be passed to fit().

+

The updated environment that will be passed to fit().

Implementation

- + 

 setMethod("preFit", "lcMethodExample", function(method, data, envir, verbose) {
@@ -120,7 +120,7 @@ 
Implementation

Estimation procedure

- +

The steps for estimating a lcMethod object are defined and executed as follows:

  1. compose(): Evaluate and finalize the method argument values.

  2. validate(): Check the validity of the method argument values in relation to the dataset.

    3. @@ -143,15 +143,15 @@

    Estimation procedure

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/predict.lcModel.html b/reference/predict.lcModel.html index e4927229..068d2898 100644 --- a/reference/predict.lcModel.html +++ b/reference/predict.lcModel.html @@ -3,7 +3,7 @@ - +
- +

lcModel predictions

- Source: R/model.R + Source: R/model.R
@@ -66,43 +66,43 @@

lcModel predictions

- 
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 predict(object, newdata = NULL, what = "mu", ..., useCluster = NA)

Arguments

-
object
+ + +
object

The lcModel object.

-
newdata
+
newdata

Optional data.frame for which to compute the model predictions. If omitted, the model training data is used. Cluster trajectory predictions are made when ids are not specified.

-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

-
...
+
...

Additional arguments.

-
useCluster
+
useCluster

Whether to use the "Cluster" column in the newdata argument for computing predictions conditional on the respective cluster. For useCluster = NA (the default), the feature is enabled if newdata contains the "Cluster" column.

Value

- - -

If newdata specifies the cluster membership; a data.frame of cluster-specific predictions. Otherwise, a list of data.frame of cluster-specific predictions is returned.

+

If newdata specifies the cluster membership; a data.frame of cluster-specific predictions. Otherwise, a list of data.frame of cluster-specific predictions is returned.

Implementation

- +

Note: Subclasses of lcModel should preferably implement predictForCluster() instead of overriding predict.lcModel as that function is designed to be easier to implement because it is single-purpose.

The predict.lcModelExt function should be able to handle the case where newdata = NULL by returning the fitted values. @@ -127,7 +127,7 @@

Implementation

See also

predictForCluster stats::predict fitted.lcModel clusterTrajectories trajectories predictPostprob predictAssignments

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -194,15 +194,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/predictAssignments.html b/reference/predictAssignments.html index ee587395..4713f22f 100644 --- a/reference/predictAssignments.html +++ b/reference/predictAssignments.html @@ -3,7 +3,7 @@ - +
- +

Predict the cluster assignments for new trajectories

- Source: R/generics.R, R/model.R + Source: R/generics.R, R/model.R
@@ -68,34 +68,34 @@

Predict the cluster assignments for new trajectories

predictAssignments(object, newdata = NULL, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 predictAssignments(object, newdata = NULL, strategy = which.max, ...)

Arguments

-
object
+ + +
object

The model.

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

-
...
+
...

Not used.

-
strategy
+
strategy

A function returning the cluster index based on the given vector of membership probabilities. By default (strategy = which.max), trajectories are assigned to the most likely cluster.

Value

- - -

A factor of length nrow(newdata) that indicates the assigned cluster per trajectory per observation.

+

A factor of length nrow(newdata) that indicates the assigned cluster per trajectory per observation.

Details

@@ -104,7 +104,7 @@

Details

See also

predictPostprob predict.lcModel

-

Other lcModel functions: +

Other lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), @@ -143,13 +143,13 @@

See also

Examples

- 
if (FALSE) {
+    
if (FALSE) { # \dontrun{
 data(latrendData)
-if (require("kml")) {
+if (require("kml")) {
   model <- latrend(method = lcMethodKML("Y", id = "Id", time = "Time"), latrendData)
   predictAssignments(model, newdata = data.frame(Id = 999, Y = 0, Time = 0))
 }
-}
+} # }
@@ -164,15 +164,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/predictForCluster.html b/reference/predictForCluster.html index 4abb40bb..3e61bd30 100644 --- a/reference/predictForCluster.html +++ b/reference/predictForCluster.html @@ -5,7 +5,7 @@ - +
- +

Predict trajectories conditional on cluster membership

- Source: R/generics.R, R/model.R + Source: R/generics.R, R/model.R
@@ -72,42 +72,42 @@

Predict trajectories conditional on cluster membership

predictForCluster(object, newdata = NULL, cluster, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 predictForCluster(object, newdata = NULL, cluster, ..., what = "mu")

Arguments

-
object
+ + +
object

The model.

-
newdata
+
newdata

A data.frame of trajectory data for which to compute trajectory assignments.

-
cluster
+
cluster

The cluster name (as character) to predict for.

-
...
+
...

Arguments passed on to predict.lcModel

useCluster

Whether to use the "Cluster" column in the newdata argument for computing predictions conditional on the respective cluster. For useCluster = NA (the default), the feature is enabled if newdata contains the "Cluster" column.

- +
-
what
+
what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

Value

- - -

A vector with the predictions per newdata observation, or a data.frame with the predictions and newdata alongside.

+

A vector with the predictions per newdata observation, or a data.frame with the predictions and newdata alongside.

Details

@@ -115,7 +115,7 @@

Details

Implementation

- +

Classes extending lcModel should override this method, unless predict.lcModel() is preferred.


 setMethod("predictForCluster", "lcModelExt",
@@ -128,7 +128,7 @@ 
Implementation

     

     
See also

     
predict.lcModel

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -528,15 +528,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/predictPostprob.html b/reference/predictPostprob.html
index e097397b..0624c618 100644
--- a/reference/predictPostprob.html
+++ b/reference/predictPostprob.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
- +

Posterior probability for new data

- Source: R/generics.R, R/model.R + Source: R/generics.R, R/model.R
@@ -70,34 +70,34 @@

Posterior probability for new data

predictPostprob(object, newdata = NULL, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 predictPostprob(object, newdata = NULL, ...)

Arguments

-
object
+ + +
object

The model.

-
newdata
+
newdata

Optional data.frame for which to compute the posterior probability. If omitted, the model training data is used.

-
...
+
...

Additional arguments passed to postprob.

Value

- - -

A N-by-K matrix indicating the posterior probability per trajectory per measurement on each row, for each cluster (the columns). +

A N-by-K matrix indicating the posterior probability per trajectory per measurement on each row, for each cluster (the columns). Here, N = nrow(newdata) and K = nClusters(object).

Implementation

- +

Classes extending lcModel should override this method to enable posterior probability predictions for new data.


 setMethod("predictPostprob", "lcModelExt", function(object, newdata = NULL, ...) {
@@ -108,7 +108,7 @@ 
Implementation

     

     
See also

     
postprob

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -157,15 +157,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/prepareData.html b/reference/prepareData.html
index 82626ac8..24904587 100644
--- a/reference/prepareData.html
+++ b/reference/prepareData.html
@@ -12,7 +12,7 @@
 
 
 
-    
+
- +

lcMethod estimation step: logic for preparing the training data

- Source: R/generics.R, R/method.R + Source: R/generics.R, R/method.R
@@ -85,40 +85,38 @@

lcMethod estimation step: logic for preparing the training data 
prepareData(method, data, verbose, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 prepareData(method, data, verbose)

Arguments

-
method
+ + +
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
verbose
+
verbose

A R.utils::Verbose object indicating the level of verbosity.

-
...
+
...

Not used.

Value

- - -

An environment.

- - +

An environment.

An environment with the prepared data variable(s) that will be passed to preFit().

Implementation

- +

A common use case for this method is when the internal method fitting procedure expects the data in a different format. In this example, the method converts the training data data.frame to a matrix of repeated and aligned trajectory measurements.


@@ -133,7 +131,7 @@ 
Implementation

Estimation procedure

- +

The steps for estimating a lcMethod object are defined and executed as follows:

  1. compose(): Evaluate and finalize the method argument values.

  2. validate(): Check the validity of the method argument values in relation to the dataset.

    3. @@ -156,15 +154,15 @@

    Estimation procedure

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/print.lcMethod.html b/reference/print.lcMethod.html index cbc8b6e5..da0a95e9 100644 --- a/reference/print.lcMethod.html +++ b/reference/print.lcMethod.html @@ -3,7 +3,7 @@ - +

- +

Print the arguments of an lcMethod object

- Source: R/method.R + Source: R/method.R
@@ -66,29 +66,31 @@

Print the arguments of an lcMethod object

- 
# S3 method for lcMethod
+    
# S3 method for class 'lcMethod'
 print(x, ..., eval = FALSE, width = 40, envir = NULL)

Arguments

-
x
+ + +
x

The lcMethod object.

-
...
+
...

Not used.

-
eval
+
eval

Whether to print the evaluated argument values.

-
width
+
width

Maximum number of characters per argument.

-
envir
+
envir

The environment in which to evaluate the arguments when eval = TRUE.

@@ -105,15 +107,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/print.lcModels.html b/reference/print.lcModels.html index 24bcf9f9..732d25a5 100644 --- a/reference/print.lcModels.html +++ b/reference/print.lcModels.html @@ -3,7 +3,7 @@ - +
- +

Print lcModels list concisely

- Source: R/models.R + Source: R/models.R
@@ -66,7 +66,7 @@

Print lcModels list concisely

- 
# S3 method for lcModels
+    
# S3 method for class 'lcModels'
 print(
   x,
   ...,
@@ -77,25 +77,27 @@ 
Print lcModels list concisely

 
     

     
Arguments

-    
x

+
+
+
x

 
The lcModels object.

 
 
-
...

+
...

 
Not used.

 
 
-
summary

+
summary

 
Whether to print the complete summary per model. This may be slow for long lists!

 
 
-
excludeShared

+
excludeShared

 
Whether to exclude model arguments which are identical across all models.

 
 

     

     
Functionality

-    
+
 
 
  • Print an argument summary for each of the models.
    • 
 
  • Convert to a data.frame of method arguments.
    • 
@@ -108,7 +110,7 @@ 
    Functionality
    
 

     

     
See also

-    
Other lcModels functions: 
+    
Other lcModels functions:
 as.lcModels(),
 lcModels,
 lcModels-class,
@@ -130,15 +132,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/qqPlot-1.png b/reference/qqPlot-1.png
index 93f2c7b9..cf293710 100644
Binary files a/reference/qqPlot-1.png and b/reference/qqPlot-1.png differ
diff --git a/reference/qqPlot.html b/reference/qqPlot.html
index 2602abd7..e94f766f 100644
--- a/reference/qqPlot.html
+++ b/reference/qqPlot.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Quantile-quantile plot

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -71,28 +71,28 @@ 
Quantile-quantile plot

 
     

     
Arguments

-    
model

+
+
+
model

 
lcModel

 
 
-
byCluster

+
byCluster

 
Whether to plot the Q-Q line per cluster

 
 
-
...

+
...

 
Additional arguments passed to residuals.lcModel, qqplotr::geom_qq_band(), qqplotr::stat_qq_line(), and qqplotr::stat_qq_point().

 
 

     

     
Value

-    
-
-
A ggplot object.

+    
A ggplot object.

     

     

     
See also

     
residuals.lcModel metric plotClusterTrajectories

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -159,15 +159,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/residuals.lcModel.html b/reference/residuals.lcModel.html
index 8093e82b..1a0d0b01 100644
--- a/reference/residuals.lcModel.html
+++ b/reference/residuals.lcModel.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract lcModel residuals

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -68,35 +68,35 @@ 
Extract lcModel residuals

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 residuals(object, ..., clusters = trajectoryAssignments(object))

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
...

+
...

 
Additional arguments.

 
 
-
clusters

+
clusters

 
Optional cluster assignments per id. If unspecified, a matrix is returned containing the cluster-specific predictions per column.

 
 

     

     
Value

-    
-
-
A numeric vector of residuals for the cluster assignments specified by clusters.
+    
A numeric vector of residuals for the cluster assignments specified by clusters.
 If the clusters argument is unspecified, a matrix of cluster-specific residuals per observations is returned.

     

     

     
See also

     
fitted.lcModel trajectories

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -145,15 +145,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/responseVariable.html b/reference/responseVariable.html
index 03738925..834dc551 100644
--- a/reference/responseVariable.html
+++ b/reference/responseVariable.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract response variable

-    Source: R/generics.R, R/method.R, R/model.R
+    Source: R/generics.R, R/method.R, R/model.R
     
     

 
@@ -70,28 +70,28 @@ 
Extract response variable

     

     
responseVariable(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 responseVariable(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 responseVariable(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The object.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A nonempty string, as character.

+    
A nonempty string, as character.

     

     

     
Details

@@ -99,7 +99,7 @@ 
Details

     

     

     
See also

-    
Other variables: 
+    
Other variables:
 idVariable(),
 timeVariable()

     

@@ -128,15 +128,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/sigma.lcModel.html b/reference/sigma.lcModel.html
index 851900de..9770e2a2 100644
--- a/reference/sigma.lcModel.html
+++ b/reference/sigma.lcModel.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract residual standard deviation from a lcModel

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -66,30 +66,30 @@ 
Extract residual standard deviation from a lcModel

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 sigma(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
...

+
...

 
Additional arguments.

 
 

     

     
Value

-    
-
-
A numeric indicating the residual standard deviation.

+    
A numeric indicating the residual standard deviation.

     

     

     
See also

     
coef.lcModel metric

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -138,15 +138,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/strip.html b/reference/strip.html
index 699ddc11..fec80461 100644
--- a/reference/strip.html
+++ b/reference/strip.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Reduce the memory footprint of an object for serialization

-    Source: R/generics.R, R/method.R, R/model.R
+    Source: R/generics.R, R/method.R, R/model.R
     
     

 
@@ -68,35 +68,35 @@ 
Reduce the memory footprint of an object for serialization

     

     
strip(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 strip(object, ..., classes = "formula")
 
-# S4 method for ANY
+# S4 method for class 'ANY'
 strip(object, ..., classes = "formula")
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 strip(object, ..., classes = "formula")

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The model.

 
 
-
...

+
...

 
Not used.

 
 
-
classes

+
classes

 
The object classes for which to remove their assigned environment. By default, only environments from formula are removed.

 
 

     

     
Value

-    
-
-
The stripped (i.e., updated) object.

+    
The stripped (i.e., updated) object.

     

     

     
Details

@@ -106,7 +106,7 @@ 
Details

     

     

     
Implementation

-    
+
 
 
Classes extending lcModel can override this method to remove additional non-essentials.

 setMethod("strip", "lcModelExt", function(object, ..., classes = "formula") {
@@ -118,7 +118,7 @@ 
Implementation

     

     

     
See also

-    
Other lcModel functions: 
+    
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -175,15 +175,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/subset.lcModels.html b/reference/subset.lcModels.html
index b00aba30..a2d7708a 100644
--- a/reference/subset.lcModels.html
+++ b/reference/subset.lcModels.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Subsetting a lcModels list based on method arguments

-    Source: R/models.R
+    Source: R/models.R
     
     

 
@@ -66,38 +66,38 @@ 
Subsetting a lcModels list based on method arguments

     

 
     

-    
# S3 method for lcModels
+    
# S3 method for class 'lcModels'
 subset(x, subset, drop = FALSE, ...)

     

 
     

     
Arguments

-    
x

+
+
+
x

 
The lcModels or list of lcModel to be subsetted.

 
 
-
subset

+
subset

 
Logical expression based on the lcModel method arguments, indicating
 which lcModel objects to keep.

 
 
-
drop

+
drop

 
Whether to return a lcModel object if the result is length 1.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A lcModels list with the subset of lcModel objects.

+    
A lcModels list with the subset of lcModel objects.

     

     

     
Functionality

-    
+
 
 
  • Print an argument summary for each of the models.
    • 
 
  • Convert to a data.frame of method arguments.
    • 
@@ -110,7 +110,7 @@ 
    Functionality
    
 

     

     
See also

-    
Other lcModels functions: 
+    
Other lcModels functions:
 as.lcModels(),
 lcModels,
 lcModels-class,
@@ -153,15 +153,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/summary.lcModel.html b/reference/summary.lcModel.html
index deb5eb4e..0c8ed0e2 100644
--- a/reference/summary.lcModel.html
+++ b/reference/summary.lcModel.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Summarize a lcModel

-    Source: R/model-summary.R
+    Source: R/model-summary.R
     
     

 
@@ -66,17 +66,19 @@ 
Summarize a lcModel

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 summary(object, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The lcModel object.

 
 
-
...

+
...

 
Additional arguments.

 
 

@@ -93,15 +95,15 @@ 
Arguments

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/test.html b/reference/test.html
index 836f927a..fe99dae6 100644
--- a/reference/test.html
+++ b/reference/test.html
@@ -4,7 +4,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Test a condition

-    Source: R/test.R
+    Source: R/test.R
     
     

 
@@ -83,48 +83,48 @@ 
Test a condition

 
     

     
Arguments

-    
key

+
+
+
key

 
Identifier for the test condition.

 
 
-
x

+
x

 
The expression to test.

 
 
-
y

+
y

 
The reference value or expression.

 
 
-
error

+
error

 
Whether to expect an error, or the pattern that the error message should match.

 
 
-
warning

+
warning

 
Whether to expect a warning, or the pattern that the warning message should match.

 
 
-
runOnly

+
runOnly

 
Whether to skip the comparison to y. If disabled, an error is raised when y is not TRUE.

 
 
-
text

+
text

 
Human-readable description of what this test condition is testing.

 
 
-
onFail

+
onFail

 
How to handle a failure of the test condition. By default, this is recorded as a test failure.
 If onFail = "skip", the condition is not tested.

 
 
-
...

+
...

 
Additional arguments passed to base::all.equal()

 
 

     

     
Value

-    
-
-
Whether the test condition has passed successfully. In case of onFail = "skip", NA is returned.

+    
Whether the test condition has passed successfully. In case of onFail = "skip", NA is returned.

     

     

     
Details

@@ -135,11 +135,11 @@ 
Details

 
     

     
Examples

-    
if (FALSE) {
+    
if (FALSE) { # \dontrun{
 test('gt', 2 > 1)
 test('eq', 1 + 1, 2)
 test('lt', 2 < 1, onFail = "warn")
-}
+} # }
 

     

   

@@ -154,15 +154,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/test.latrend.html b/reference/test.latrend.html
index 66558f65..7a41605a 100644
--- a/reference/test.latrend.html
+++ b/reference/test.latrend.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Test the implementation of an lcMethod and associated lcModel subclasses

-    Source: R/test.R
+    Source: R/test.R
     
     

 
@@ -81,17 +81,19 @@ 
Test the implementation of an lcMethod and associated lcModel subclasses

     
Arguments

-    
class

+
+
+
class

 
The name of the lcMethod subclass to test.
 The class should inherit from lcMethod.

 
 
-
instantiator

+
instantiator

 
A function with signature (id, time, response, ...),
 returning an object inheriting from the lcMethod specified by the class argument.

 
 
-
data

+
data

 
An optional dataset comprising three highly distinct constant clusters that will be used for testing, represented by a data.frame.
 The data.frame must contain the columns "Id", "Time", "Value", "Cluster" of types character, numeric, numeric, and character, respectively.
 All trajectories should be of equal length and have observations at the same moments in time.
@@ -99,28 +101,28 @@ 
Arguments

 This enables tests to insert additional observations as needed by sampling from the available observations.

 
 
-
args

+
args

 
Other arguments passed to the instantiator function.

 
 
-
tests

+
tests

 
A character vector indicating the type of tests to run, as defined in the *.Rraw files inside the /test/ folder.

 
 
-
maxFails

+
maxFails

 
The maximum number of allowed test condition failures before testing is ended prematurely.

 
 
-
errorOnFail

+
errorOnFail

 
Whether to throw the test errors as an error. This is always enabled while running package tests.

 
 
-
clusterRecovery

+
clusterRecovery

 
Whether to test for correct recovery/identification of the original clusters in the test data.
 By default, a warning is outputted.

 
 
-
verbose

+
verbose

 
Whether the output testing results. This is always disabled while running package tests.

 
 

@@ -155,15 +157,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/time.lcModel.html b/reference/time.lcModel.html
index 698ecba7..39eaf24d 100644
--- a/reference/time.lcModel.html
+++ b/reference/time.lcModel.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Sampling times of a lcModel

-    Source: R/model.R
+    Source: R/model.R
     
     

 
@@ -66,30 +66,30 @@ 
Sampling times of a lcModel

     

 
     

-    
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 time(x, ...)

     

 
     

     
Arguments

-    
x

+
+
+
x

 
The lcModel object.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
A numeric vector of the unique times at which observations occur, in increasing order.

+    
A numeric vector of the unique times at which observations occur, in increasing order.

     

     

     
See also

     
timeVariable model.data

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -138,15 +138,15 @@ 
See also

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/timeVariable.html b/reference/timeVariable.html
index aaab5f51..03b72177 100644
--- a/reference/timeVariable.html
+++ b/reference/timeVariable.html
@@ -3,7 +3,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Extract the time variable

-    Source: R/generics.R, R/method.R, R/model.R, and 1 more
+    Source: R/generics.R, R/method.R, R/model.R, and 1 more
     
     

 
@@ -68,35 +68,35 @@ 
Extract the time variable

     

     
timeVariable(object, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 timeVariable(object, ...)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 timeVariable(object)
 
-# S4 method for ANY
+# S4 method for class 'ANY'
 timeVariable(object)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The object.

 
 
-
...

+
...

 
Not used.

 
 

     

     
Value

-    
-
-
The time variable name, as character.

+    
The time variable name, as character.

     

     

     
See also

-    
Other variables: 
+    
Other variables:
 idVariable(),
 responseVariable()

     

@@ -125,15 +125,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/trajectories.html b/reference/trajectories.html
index 02199b59..d96dc1d0 100644
--- a/reference/trajectories.html
+++ b/reference/trajectories.html
@@ -6,7 +6,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get the trajectories

-    Source: R/generics.R, R/trajectories.R, R/model.R
+    Source: R/generics.R, R/trajectories.R, R/model.R
     
     

 
@@ -81,7 +81,7 @@ 
Get the trajectories

   ...
 )
 
-# S4 method for data.frame
+# S4 method for class 'data.frame'
 trajectories(
   object,
   id = idVariable(object),
@@ -91,7 +91,7 @@ 
Get the trajectories

   ...
 )
 
-# S4 method for matrix
+# S4 method for class 'matrix'
 trajectories(
   object,
   id = idVariable(object),
@@ -101,10 +101,10 @@ 
Get the trajectories

   ...
 )
 
-# S4 method for call
+# S4 method for class 'call'
 trajectories(object, ..., envir)
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 trajectories(
   object,
   id = idVariable(object),
@@ -117,39 +117,39 @@ 
Get the trajectories

 
     

     
Arguments

-    
object

+
+
+
object

 
The data or model or extract the trajectories from.

 
 
-
id

+
id

 
The identifier variable name, see idVariable.

 
 
-
time

+
time

 
The time variable name, see timeVariable.

 
 
-
response

+
response

 
The response variable name, see responseVariable.

 
 
-
cluster

+
cluster

 
Experimental feature for data.frame input: a vector of cluster membership per id

 
 
-
...

+
...

 
Arguments passed to trajectoryAssignments for generating the Cluster column.

 
 
-
envir

+
envir

 
The environment used to evaluate the data object in (e.g., in case object is of type call).

 
 

     

     
Value

-    
-
-
A data.frame with columns matching the id, time, response and cluster name arguments.

+    
A data.frame with columns matching the id, time, response and cluster name arguments.

     

     

     
Details

@@ -2182,15 +2182,15 @@ 
Examples

 

 
 

-  
Site built with pkgdown 2.0.9.

+  
Site built with pkgdown 2.1.1.

 

 
       

 
-  
 
 
-  
+
+
 
   
 
diff --git a/reference/trajectoryAssignments.html b/reference/trajectoryAssignments.html
index 369403f1..f16e9656 100644
--- a/reference/trajectoryAssignments.html
+++ b/reference/trajectoryAssignments.html
@@ -7,7 +7,7 @@
 
 
 
-    
+
 
     

       

   

 

 
-      
+
 
       

   

     

     
Get the cluster membership of each trajectory

-    Source: R/generics.R, R/matrix.R, R/model.R
+    Source: R/generics.R, R/matrix.R, R/model.R
     
     

 
@@ -76,7 +76,7 @@ 
Get the cluster membership of each trajectory

     

     
trajectoryAssignments(object, ...)
 
-# S4 method for matrix
+# S4 method for class 'matrix'
 trajectoryAssignments(
   object,
   strategy = which.max,
@@ -84,34 +84,34 @@ 
Get the cluster membership of each trajectory

   ...
 )
 
-# S4 method for lcModel
+# S4 method for class 'lcModel'
 trajectoryAssignments(object, strategy = which.max, ...)

     

 
     

     
Arguments

-    
object

+
+
+
object

 
The model.

 
 
-
...

+
...

 
Any additional arguments passed to the strategy function.

 
 
-
strategy

+
strategy

 
A function returning the cluster index based on the given vector of membership probabilities. By default, ids are assigned to the cluster with the highest probability.

 
 
-
clusterNames

+
clusterNames

 
Optional character vector with the cluster names.
 If clusterNames = NULL, make.clusterNames() is used.

 
 

     

     
Value

-    
-
-
A factor vector indicating the cluster membership for each trajectory.

+    
A factor vector indicating the cluster membership for each trajectory.

     

     

     
Details

@@ -121,7 +121,7 @@ 
Details

     

     
See also

     
postprob clusterSizes predictAssignments

-
Other lcModel functions: 
+
Other lcModel functions:
 clusterNames(),
 clusterProportions(),
 clusterSizes(),
@@ -164,22 +164,22 @@ 
Examples

 method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
 model <- latrend(method, latrendData)
 trajectoryAssignments(model)
-#>   [1] A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
-#>  [38] A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
-#>  [75] A A A A A A B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
-#> [112] B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
-#> [149] B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
-#> [186] B B B B B B B B B B B B B B B
+#>   [1] B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
+#>  [38] B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
+#>  [75] B B B B B B A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
+#> [112] A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
+#> [149] A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
+#> [186] A A A A A A A A A A A A A A A
 #> Levels: A B
 
 # assign trajectories at random using weighted sampling
 trajectoryAssignments(model, strategy = which.weight)
-#>   [1] A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
-#>  [38] A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
-#>  [75] A A A A A A B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
-#> [112] B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
-#> [149] B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
-#> [186] B B B B B B B B B B B B B B B
+#>   [1] B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
+#>  [38] B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
+#>  [75] B B B B B B A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
+#> [112] A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
+#> [149] A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
+#> [186] A A A A A A A A A A A A A A A
 #> Levels: A B
@@ -195,15 +195,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/transformFitted.html b/reference/transformFitted.html index 9a0d0250..333ea2ad 100644 --- a/reference/transformFitted.html +++ b/reference/transformFitted.html @@ -20,7 +20,7 @@ - +
- +

Helper function for custom lcModel classes implementing fitted.lcModel()

- Source: R/model-transform.R + Source: R/model-transform.R
@@ -99,42 +99,42 @@

Helper function for custom lcModel classes implementing fitted.lcModel()

transformFitted(pred, model, clusters)
 
-# S4 method for NULL,lcModel
+# S4 method for class 'NULL,lcModel'
 transformFitted(pred, model, clusters = NULL)
 
-# S4 method for matrix,lcModel
+# S4 method for class 'matrix,lcModel'
 transformFitted(pred, model, clusters = NULL)
 
-# S4 method for list,lcModel
+# S4 method for class 'list,lcModel'
 transformFitted(pred, model, clusters = NULL)
 
-# S4 method for data.frame,lcModel
+# S4 method for class 'data.frame,lcModel'
 transformFitted(pred, model, clusters = NULL)

Arguments

-
pred
+ + +
pred

The cluster-specific predictions for each observation

-
model
+
model

The lcModel by which the prediction was made.

-
clusters
+
clusters

The trajectory cluster assignment per observation. Optional.

Value

- - -

If the clusters argument was specified, a vector of fitted values conditional on the given cluster assignment. Else, a matrix with the fitted values per cluster per column.

+

If the clusters argument was specified, a vector of fitted values conditional on the given cluster assignment. Else, a matrix with the fitted values per cluster per column.

Example implementation

- +

A typical implementation of fitted.lcModel() for your own lcModel class would have the following format:


 fitted.lcModelExample <- function(object,
@@ -159,15 +159,15 @@ 
Example implementation
-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/transformPredict.html b/reference/transformPredict.html index 2344ba62..48ad4ffb 100644 --- a/reference/transformPredict.html +++ b/reference/transformPredict.html @@ -22,7 +22,7 @@ - +
- +

Helper function for custom lcModel classes implementing predict.lcModel()

- Source: R/model-transform.R + Source: R/model-transform.R
@@ -103,42 +103,42 @@

Helper function for custom lcModel classes implementing predict.lcModel() 
transformPredict(pred, model, newdata)
 
-# S4 method for NULL,lcModel
+# S4 method for class 'NULL,lcModel'
 transformPredict(pred, model, newdata)
 
-# S4 method for vector,lcModel
+# S4 method for class 'vector,lcModel'
 transformPredict(pred, model, newdata)
 
-# S4 method for matrix,lcModel
+# S4 method for class 'matrix,lcModel'
 transformPredict(pred, model, newdata)
 
-# S4 method for data.frame,lcModel
+# S4 method for class 'data.frame,lcModel'
 transformPredict(pred, model, newdata)

Arguments

-
pred
+ + +
pred

The (per-cluster) predictions for newdata.

-
model
+
model

The lcModel for which the prediction was made.

-
newdata
+
newdata

A data.frame containing the input data to predict for.

Value

- - -

A data.frame with the predictions, or a list of cluster-specific prediction data.frames.

+

A data.frame with the predictions, or a list of cluster-specific prediction data.frames.

Example implementation

- +

In case we have a custom lcModel class based on an existing internal model representation with a predict() function, we can use transformPredict() to easily transform the internal model predictions to the right format. @@ -172,15 +172,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/tsframe.html b/reference/tsframe.html index 55b09b14..f1bab176 100644 --- a/reference/tsframe.html +++ b/reference/tsframe.html @@ -3,7 +3,7 @@ - +
- +

Convert a multiple time series matrix to a data.frame

- Source: R/matrix.R + Source: R/matrix.R
@@ -89,40 +89,40 @@

Convert a multiple time series matrix to a data.frame

Arguments

-
data
+ + +
data

The matrix containing a trajectory on each row.

-
response
+
response

The response column name.

-
id
+
id

The id column name.

-
time
+
time

The time column name.

-
ids
+
ids

A vector specifying the id names. Should match the number of rows of data.

-
times
+
times

A numeric vector specifying the times of the measurements. Should match the number of columns of data.

-
as.data.table
+
as.data.table

Whether to return the result as a data.table, or a data.frame otherwise.

Value

- - -

A data.table or data.frame containing the repeated measures.

+

A data.table or data.frame containing the repeated measures.

Note

@@ -146,15 +146,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/tsmatrix.html b/reference/tsmatrix.html index 4f52caf3..334c76d3 100644 --- a/reference/tsmatrix.html +++ b/reference/tsmatrix.html @@ -4,7 +4,7 @@ - +
- +

Convert a longitudinal data.frame to a matrix

- Source: R/matrix.R + Source: R/matrix.R
@@ -87,23 +87,25 @@

Convert a longitudinal data.frame to a matrix

Arguments

-
data
+ + +
data

The matrix containing a trajectory on each row.

-
response
+
response

The response column name.

-
id
+
id

The id column name.

-
time
+
time

The time column name.

-
fill
+
fill

A scalar value. If FALSE, an error is thrown when time series observations are missing in the data frame. Otherwise, the value used for representing missing observations.

@@ -111,9 +113,7 @@

Arguments

Value

- - -

A matrix with a trajectory per row.

+

A matrix with a trajectory per row.

Note

@@ -137,15 +137,15 @@

See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/update.lcMethod.html b/reference/update.lcMethod.html index 78b5ae4d..fbcbfeb3 100644 --- a/reference/update.lcMethod.html +++ b/reference/update.lcMethod.html @@ -3,7 +3,7 @@ - +
- +

Update a method specification

- Source: R/method.R + Source: R/method.R
@@ -66,37 +66,37 @@

Update a method specification

- 
# S3 method for lcMethod
+    
# S3 method for class 'lcMethod'
 update(object, ..., .eval = FALSE, .remove = character(), envir = NULL)

Arguments

-
object
+ + +
object

The lcMethod object.

-
...
+
...

The new or updated method argument values.

-
.eval
+
.eval

Whether to assign the evaluated argument values to the method. By default (FALSE), the argument expression is preserved.

-
.remove
+
.remove

Names of arguments that should be removed.

-
envir
+
envir

The environment in which to evaluate the arguments. If NULL, the environment associated with the object is used. If not available, the parent.frame() is used.

Value

- - -

The new lcMethod object with the additional or updated arguments.

+

The new lcMethod object with the additional or updated arguments.

Details

@@ -104,7 +104,7 @@

Details

See also

-

Other lcMethod functions: +

Other lcMethod functions: [[,lcMethod-method, as.data.frame.lcMethod(), as.data.frame.lcMethods(), @@ -142,15 +142,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/update.lcModel.html b/reference/update.lcModel.html index ee07e436..65145e71 100644 --- a/reference/update.lcModel.html +++ b/reference/update.lcModel.html @@ -3,7 +3,7 @@ - +
- +

Update a lcModel

- Source: R/model.R + Source: R/model.R
@@ -66,17 +66,19 @@

Update a lcModel

- 
# S3 method for lcModel
+    
# S3 method for class 'lcModel'
 update(object, ...)

Arguments

-
object
+ + +
object

The lcModel object.

-
...
+
...

Arguments passed on to latrend

method

An lcMethod object specifying the longitudinal cluster method to apply, or the name (as character) of the lcMethod subclass to instantiate.

@@ -94,15 +96,13 @@

Arguments

a numeric indicating the verbosity level (see Verbose), or one of c('info', 'fine', 'finest').

- +

Value

- - -

The refitted lcModel object, of the same type as the object argument.

+

The refitted lcModel object, of the same type as the object argument.

See also

@@ -131,15 +131,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/validate.html b/reference/validate.html index d1144b93..18869573 100644 --- a/reference/validate.html +++ b/reference/validate.html @@ -12,7 +12,7 @@ - +
- +

lcMethod estimation step: method argument validation logic

- Source: R/generics.R, R/method.R + Source: R/generics.R, R/method.R
@@ -83,38 +83,38 @@

lcMethod estimation step: method argument validation logic

validate(method, data, envir, ...)
 
-# S4 method for lcMethod
+# S4 method for class 'lcMethod'
 validate(method, data, envir = NULL, ...)

Arguments

-
method
+ + +
method

An object inheriting from lcMethod with all its arguments having been evaluated and finalized.

-
data
+
data

A data.frame representing the transformed training data.

-
envir
+
envir

The environment in which the lcMethod should be evaluated

-
...
+
...

Not used.

Value

- - -

Either TRUE if all validation checks passed, +

Either TRUE if all validation checks passed, or a scalar character containing a description of the failed validation checks.

Implementation

- +

An example implementation checking for the existence of specific arguments and type:


 
@@ -130,7 +130,7 @@ 
Implementation

Estimation procedure

- +

The steps for estimating a lcMethod object are defined and executed as follows:

  1. compose(): Evaluate and finalize the method argument values.

  2. validate(): Check the validity of the method argument values in relation to the dataset.

    3. @@ -157,15 +157,15 @@

    See also

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/weighted.meanNA.html b/reference/weighted.meanNA.html index 89b9afec..565cede9 100644 --- a/reference/weighted.meanNA.html +++ b/reference/weighted.meanNA.html @@ -3,7 +3,7 @@ - +
- +

Weighted arithmetic mean ignoring NAs

- Source: R/methodStratify.R + Source: R/methodStratify.R
@@ -71,17 +71,19 @@

Weighted arithmetic mean ignoring NAs

Arguments

-
x
+ + +
x

an object containing the values whose weighted mean is to be computed.

-
w
+
w

a numerical vector of weights the same length as x giving the weights to use for elements of x.

-
...
+
...

arguments to be passed to or from methods.

@@ -98,15 +100,15 @@

Arguments

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

- - + + diff --git a/reference/which.weight.html b/reference/which.weight.html index ff99fe07..1a3c782e 100644 --- a/reference/which.weight.html +++ b/reference/which.weight.html @@ -3,7 +3,7 @@ - +
- +

Sample an index of a vector weighted by the elements

- Source: R/random.R + Source: R/random.R
@@ -71,22 +71,22 @@

Sample an index of a vector weighted by the elements

Arguments

-
x
+ + +
x

A positive numeric vector.

Value

- - -

An integer giving the index of the sampled element.

+

An integer giving the index of the sampled element.

Examples

x = c(.01, .69, .3)
 which.weight(x) #1, 2, or 3
-#> [1] 3
+#> [1] 2
@@ -101,15 +101,15 @@

Examples

-

Site built with pkgdown 2.0.9.

+

Site built with pkgdown 2.1.1.

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