fix #286: stan_glm output not duplicated (#344)

* fix #286 * update readme * Apply automatic changes --------- Co-authored-by: rempsyc <[email protected]>
easystats · Mar 6, 2023 · d52fa42 · d52fa42
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diff --git a/R/report.stanreg.R b/R/report.stanreg.R
@@ -80,6 +80,7 @@ report_priors.stanreg <- function(x, ...) {
     )
   )
 
+  values <- paste0(values, collapse = "; ")
   values <- paste0(params$Prior_Distribution, " (", values, ")")
 
   if (length(unique(values)) == 1 && nrow(params) > 1) {

diff --git a/README.Rmd b/README.Rmd
@@ -217,23 +217,16 @@ model <- stan_glm(mpg ~ qsec + wt, data = mtcars, refresh = 0, iter = 1000) %>%
 
 One can, for complex reports, directly access the pieces of the reports:
 
-```{r, eval=FALSE}
+```{r}
 model <- lm(Sepal.Length ~ Species, data = iris)
 
 report_model(model)
-report_performance(model)
-report_statistics(model)
-```
 
-```{r echo=FALSE}
-model <- lm(Sepal.Length ~ Species, data = iris)
+report_performance(model)
 
-print(report_model(model), width = 80)
-print(report_performance(model), width = 80)
-print(report_statistics(model), width = 80)
+report_statistics(model)
 ```
 
-
 ### Report participants' details
 
 This can be useful to complete the **Participants** paragraph of your manuscript.
@@ -250,7 +243,7 @@ paste(
 )
 ```
 
-```{r echo=FALSE}
+```{r echo=F}
 data <- data.frame(
   "Age" = c(22, 23, 54, 21),
   "Sex" = c("F", "F", "M", "M")
@@ -259,15 +252,16 @@ data <- data.frame(
 paste(
   report_participants(data, spell_n = TRUE),
   "were recruited in the study by means of torture and coercion."
-) %>%
-  print(width = 80)
+) |>
+  insight::format_message() %>%
+  cat()
 ```
 
 ### Report sample
 
 Report can also help you create a sample description table (also referred to as **Table 1**).
 
-```{r, include=FALSE}
+```{r, eval=FALSE}
 report_sample(iris, group_by = "Species")
 ```
 

diff --git a/README.md b/README.md
@@ -260,46 +260,20 @@ report(model)
 
     # We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
     # of 1000 iterations and a warmup of 500) to predict mpg with qsec and wt
-    # (formula: mpg ~ qsec + wt). Priors over parameters were set as normal (mean =
-    # 0.00, SD = 8.43) distributions. The model's explanatory power is substantial
-    # (R2 = 0.81, 95% CI [0.70, 0.89], adj. R2 = 0.79). The model's intercept,
-    # corresponding to qsec = 0 and wt = 0, is at 19.53 (95% CI [9.69, 30.43]).
-    # Within this model:
+    # (formula: mpg ~ qsec + wt). Priors over parameters were all set as normal (mean
+    # = 0.00, SD = 8.43; mean = 0.00, SD = 15.40) distributions. The model's
+    # explanatory power is substantial (R2 = 0.81, 95% CI [0.70, 0.90], adj. R2 =
+    # 0.79). The model's intercept, corresponding to qsec = 0 and wt = 0, is at 19.96
+    # (95% CI [8.93, 31.01]). Within this model:
     # 
-    #   - The effect of qsec (Median = 0.93, 95% CI [0.39, 1.45]) has a 99.95%
-    # probability of being positive (> 0), 99.05% of being significant (> 0.30), and
-    # 0.10% of being large (> 1.81). The estimation successfully converged (Rhat =
-    # 1.000) and the indices are reliable (ESS = 1974)
-    #   - The effect of wt (Median = -5.04, 95% CI [-6.04, -4.10]) has a 100.00%
+    #   - The effect of qsec (Median = 0.92, 95% CI [0.34, 1.47]) has a 99.85%
+    # probability of being positive (> 0), 98.25% of being significant (> 0.30), and
+    # 0.05% of being large (> 1.81). The estimation successfully converged (Rhat =
+    # 0.999) and the indices are reliable (ESS = 2268)
+    #   - The effect of wt (Median = -5.04, 95% CI [-6.07, -4.12]) has a 100.00%
     # probability of being negative (< 0), 100.00% of being significant (< -0.30),
     # and 100.00% of being large (< -1.81). The estimation successfully converged
-    # (Rhat = 1.000) and the indices are reliable (ESS = 2233)
-    # 
-    # Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
-    # framework, we report the median of the posterior distribution and its 95% CI
-    # (Highest Density Interval), along the probability of direction (pd), the
-    # probability of significance and the probability of being large. The thresholds
-    # beyond which the effect is considered as significant (i.e., non-negligible) and
-    # large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
-    # outcome's SD). Convergence and stability of the Bayesian sampling has been
-    # assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
-    # Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
-    # and We fitted a Bayesian linear model (estimated using MCMC sampling with 4
-    # chains of 1000 iterations and a warmup of 500) to predict mpg with qsec and wt
-    # (formula: mpg ~ qsec + wt). Priors over parameters were set as normal (mean =
-    # 0.00, SD = 15.40) distributions. The model's explanatory power is substantial
-    # (R2 = 0.81, 95% CI [0.70, 0.89], adj. R2 = 0.79). The model's intercept,
-    # corresponding to qsec = 0 and wt = 0, is at 19.53 (95% CI [9.69, 30.43]).
-    # Within this model:
-    # 
-    #   - The effect of qsec (Median = 0.93, 95% CI [0.39, 1.45]) has a 99.95%
-    # probability of being positive (> 0), 99.05% of being significant (> 0.30), and
-    # 0.10% of being large (> 1.81). The estimation successfully converged (Rhat =
-    # 1.000) and the indices are reliable (ESS = 1974)
-    #   - The effect of wt (Median = -5.04, 95% CI [-6.04, -4.10]) has a 100.00%
-    # probability of being negative (< 0), 100.00% of being significant (< -0.30),
-    # and 100.00% of being large (< -1.81). The estimation successfully converged
-    # (Rhat = 1.000) and the indices are reliable (ESS = 2233)
+    # (Rhat = 1.002) and the indices are reliable (ESS = 2074)
     # 
     # Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
     # framework, we report the median of the posterior distribution and its 95% CI
@@ -321,17 +295,18 @@ One can, for complex reports, directly access the pieces of the reports:
 model <- lm(Sepal.Length ~ Species, data = iris)
 
 report_model(model)
+# linear model (estimated using OLS) to predict Sepal.Length with Species (formula: Sepal.Length ~ Species)
+
 report_performance(model)
+# The model explains a statistically significant and substantial proportion of
+# variance (R2 = 0.62, F(2, 147) = 119.26, p < .001, adj. R2 = 0.61)
+
 report_statistics(model)
+# beta = 5.01, 95% CI [4.86, 5.15], t(147) = 68.76, p < .001; Std. beta = -1.01, 95% CI [-1.18, -0.84]
+# beta = 0.93, 95% CI [0.73, 1.13], t(147) = 9.03, p < .001; Std. beta = 1.12, 95% CI [0.88, 1.37]
+# beta = 1.58, 95% CI [1.38, 1.79], t(147) = 15.37, p < .001; Std. beta = 1.91, 95% CI [1.66, 2.16]
 ```
 
-    # linear model (estimated using OLS) to predict Sepal.Length with Species (formula: Sepal.Length ~ Species)
-    # The model explains a statistically significant and substantial proportion of
-    # variance (R2 = 0.62, F(2, 147) = 119.26, p < .001, adj. R2 = 0.61)
-    # beta = 5.01, 95% CI [4.86, 5.15], t(147) = 68.76, p < .001; Std. beta = -1.01, 95% CI [-1.18, -0.84]
-    # beta = 0.93, 95% CI [0.73, 1.13], t(147) = 9.03, p < .001; Std. beta = 1.12, 95% CI [0.88, 1.37]
-    # beta = 1.58, 95% CI [1.38, 1.79], t(147) = 15.37, p < .001; Std. beta = 1.91, 95% CI [1.66, 2.16]
-
 ### Report participants’ details
 
 This can be useful to complete the **Participants** paragraph of your
@@ -349,13 +324,19 @@ paste(
 )
 ```
 
-    # [1] "Four participants (Mean age = 30.0, SD = 16.0, range: [21, 54]; Sex: 50.0% females, 50.0% males, 0.0% other) were recruited in the study by means of torture and coercion."
+    # Four participants (Mean age = 30.0, SD = 16.0, range: [21, 54]; Sex:
+    #   50.0% females, 50.0% males, 0.0% other) were recruited in the study by
+    #   means of torture and coercion.
 
 ### Report sample
 
 Report can also help you create a sample description table (also
 referred to as **Table 1**).
 
+``` r
+report_sample(iris, group_by = "Species")
+```
+
 | Variable               | setosa (n=50) | versicolor (n=50) | virginica (n=50) | Total (n=150) |
 |:-----------------------|:--------------|:------------------|:-----------------|:--------------|
 | Mean Sepal.Length (SD) | 5.01 (0.35)   | 5.94 (0.52)       | 6.59 (0.64)      | 5.84 (0.83)   |
@@ -377,7 +358,7 @@ report(sessionInfo())
     # 1.1.31; Bates D et al., 2015), Matrix (version 1.5.3; Bates D et al., 2022),
     # Rcpp (version 1.0.9; Eddelbuettel D, François R, 2011), rstanarm (version
     # 2.21.3; Goodrich B et al., 2022), report (version 0.5.6; Makowski D et al.,
-    # 2023) and dplyr (version 1.0.10; Wickham H et al., 2022).
+    # 2023) and dplyr (version 1.1.0; Wickham H et al., 2023).
     # 
     # References
     # ----------
@@ -409,8 +390,8 @@ report(sessionInfo())
     #   - R Core Team (2022). _R: A Language and Environment for Statistical
     # Computing_. R Foundation for Statistical Computing, Vienna, Austria.
     # <https://www.R-project.org/>.
-    #   - Wickham H, François R, Henry L, Müller K (2022). _dplyr: A Grammar of Data
-    # Manipulation_. R package version 1.0.10,
+    #   - Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar
+    # of Data Manipulation_. R package version 1.1.0,
     # <https://CRAN.R-project.org/package=dplyr>.
 
 ## Credits

diff --git a/tests/testthat/_snaps/windows/report.stanreg.md b/tests/testthat/_snaps/windows/report.stanreg.md
@@ -5,37 +5,11 @@
     Output
       We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
       of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
-      (formula: mpg ~ qsec + wt). Priors over parameters were set as normal (mean =
-      0.00, SD = 8.43) distributions. The model's explanatory power is substantial
-      (R2 = 0.81, 95% CI [0.70, 0.90], adj. R2 = 0.80). The model's intercept,
-      corresponding to qsec = 0 and wt = 0, is at 19.71 (95% CI [9.04, 30.18]).
-      Within this model:
-      
-        - The effect of qsec (Median = 0.92, 95% CI [0.40, 1.47]) has a 99.83%
-      probability of being positive (> 0), 99.00% of being significant (> 0.30), and
-      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
-      0.999) but the indices are unreliable (ESS = 451)
-        - The effect of wt (Median = -5.06, 95% CI [-6.02, -4.18]) has a 100.00%
-      probability of being negative (< 0), 100.00% of being significant (< -0.30),
-      and 100.00% of being large (< -1.81). However, the estimation might not have
-      successfuly converged (Rhat = 1.013) and the indices are unreliable (ESS = 478)
-      
-      Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
-      framework, we report the median of the posterior distribution and its 95% CI
-      (Highest Density Interval), along the probability of direction (pd), the
-      probability of significance and the probability of being large. The thresholds
-      beyond which the effect is considered as significant (i.e., non-negligible) and
-      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
-      outcome's SD). Convergence and stability of the Bayesian sampling has been
-      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
-      Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
-      and We fitted a Bayesian linear model (estimated using MCMC sampling with 4
-      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
-      (formula: mpg ~ qsec + wt). Priors over parameters were set as normal (mean =
-      0.00, SD = 15.40) distributions. The model's explanatory power is substantial
-      (R2 = 0.81, 95% CI [0.70, 0.90], adj. R2 = 0.80). The model's intercept,
-      corresponding to qsec = 0 and wt = 0, is at 19.71 (95% CI [9.04, 30.18]).
-      Within this model:
+      (formula: mpg ~ qsec + wt). Priors over parameters were all set as normal (mean
+      = 0.00, SD = 8.43; mean = 0.00, SD = 15.40) distributions. The model's
+      explanatory power is substantial (R2 = 0.81, 95% CI [0.70, 0.90], adj. R2 =
+      0.80). The model's intercept, corresponding to qsec = 0 and wt = 0, is at 19.71
+      (95% CI [9.04, 30.18]). Within this model:
       
         - The effect of qsec (Median = 0.92, 95% CI [0.40, 1.47]) has a 99.83%
       probability of being positive (> 0), 99.00% of being significant (> 0.30), and
-Original file line number
+Diff line change
@@ Expand Up / @@ -80,6 +80,7 @@ report_priors.stanreg <- function(x, ...) { @@
         )
       )
+      values <- paste0(values, collapse = "; ")
       values <- paste0(params$Prior_Distribution, " (", values, ")")
       if (length(unique(values)) == 1 && nrow(params) > 1) {
@@ Expand Down @@