README + report_priors()

DESCRIPTION

@@ -86,6 +86,7 @@ Collate:
     'report_parameters.R'
     'report_participants.R'
     'report_performance.R'
+    'report_priors.R'
     'report_random.R'
     'report_sample.R'
     'report_statistics.R'

NAMESPACE

@@ -13,6 +13,7 @@ S3method(print,report_intercept)
 S3method(print,report_model)
 S3method(print,report_parameters)
 S3method(print,report_performance)
+S3method(print,report_priors)
 S3method(print,report_random)
 S3method(print,report_statistics)
 S3method(print,report_table)
@@ -117,6 +118,8 @@ S3method(report_performance,merMod)
 S3method(report_performance,stanreg)
 S3method(report_performance,survreg)
 S3method(report_performance,zeroinfl)
+S3method(report_priors,default)
+S3method(report_priors,stanreg)
 S3method(report_random,MixMod)
 S3method(report_random,default)
 S3method(report_random,glmmTMB)
@@ -190,6 +193,7 @@ S3method(summary,report_intercept)
 S3method(summary,report_model)
 S3method(summary,report_parameters)
 S3method(summary,report_performance)
+S3method(summary,report_priors)
 S3method(summary,report_random)
 S3method(summary,report_statistics)
 S3method(summary,report_table)
@@ -201,6 +205,7 @@ export(as.report_intercept)
 export(as.report_model)
 export(as.report_parameters)
 export(as.report_performance)
+export(as.report_priors)
 export(as.report_random)
 export(as.report_statistics)
 export(as.report_table)
@@ -229,6 +234,7 @@ export(report_packages)
 export(report_parameters)
 export(report_participants)
 export(report_performance)
+export(report_priors)
 export(report_random)
 export(report_sample)
 export(report_statistics)

R/report.lm.R

@@ -405,6 +405,14 @@ report_model.lm <- function(x, table=NULL, ...) {
     text <- paste0(text, ". ", summary(random_text))
   }
 
+  # Bayesian
+  if(info$is_bayesian){
+    priors_text <- report_priors(x)
+    text_full <- paste0(text_full, ". ", as.character(priors_text))
+    text <- paste0(text, ". ", summary(priors_text))
+  }
+
+
   as.report_model(text_full, summary = text, ...)
 }
 

R/report.stanreg.R

@@ -11,7 +11,7 @@
 #'
 #' # Bayesian models
 #' if(require("rstanarm")){
-#'   model <- stan_glm(mpg ~ cyl + wt, data = mtcars, refresh=0, iter=600)
+#'   model <- stan_glm(mpg ~ qsec + wt, data = mtcars, refresh=0, iter=600)
 #'   r <- report(model)
 #'   r
 #'   summary(r)
@@ -52,6 +52,34 @@ report_text.stanreg <- report_text.lm
 # ==================== Specific to Bayes ===================================
 
 
+# report_priors -----------------------------------------------------------
+
+#' @export
+report_priors.stanreg <- function(x) {
+  params <- bayestestR::describe_prior(x)
+  params <- params[params$Parameter != "(Intercept)", ]
+
+  # Return empty if no priors info
+  if (!"Prior_Distribution" %in% names(params) | nrow(params) == 0) {
+    return("")
+  }
+
+  values <- ifelse(params$Prior_Distribution == "normal",
+                   paste0("mean = ", insight::format_value(params$Prior_Location), ", SD = ", insight::format_value(params$Prior_Scale)),
+                   paste0("location = ", insight::format_value(params$Prior_Location), ", scale = ", insight::format_value(params$Prior_Scale))
+  )
+
+  values <- paste0(params$Prior_Distribution, " (", values, ")")
+
+  if (length(unique(values)) == 1 & nrow(params) > 1) {
+    text <- paste0("all set as ", values[1])
+  } else {
+    text <- paste0("set as ", format_text(values))
+  }
+
+  text <- paste0(" Priors over parameters were ", text, " distributions.")
+  as.report_priors(text)
+}
 
 
 # report_parameters -------------------------------------------------------

R/report_priors.R

@@ -0,0 +1,63 @@
+#' Report priors of Bayesian models
+#'
+#' Reports priors of Bayesian models (see list of supported objects in \code{\link{report}}).
+#'
+#' @inheritParams report
+#' @inheritParams report_table
+#' @inheritParams report_text
+#' @inheritParams as.report
+#'
+#' @return A \code{character} string.
+#'
+#' @examples
+#' library(report)
+#'
+#' # Bayesian models
+#' if(require("rstanarm")){
+#'   model <- stan_glm(mpg ~ disp, data = mtcars, refresh=0, iter=1000)
+#'   r <- report_priors(model)
+#'   r
+#'   summary(r)
+#' }
+#' @export
+report_priors <- function(x, ...) {
+  UseMethod("report_priors")
+}
+
+
+#' @export
+report_priors.default <- function(x, ...) {
+  stop(paste0("report_priors() is not available for objects of class ", class(x)))
+}
+
+# METHODS -----------------------------------------------------------------
+
+
+#' @rdname as.report
+#' @export
+as.report_priors <- function(x, summary = NULL, ...) {
+  class(x) <- unique(c("report_priors", class(x)))
+  attributes(x) <- c(attributes(x), list(...))
+
+  if (!is.null(summary)) {
+    attr(x, "summary") <- summary
+  }
+  x
+}
+
+
+#' @export
+summary.report_priors <- function(object, ...) {
+  if(is.null(attributes(object)$summary)){
+    object
+  } else{
+    attributes(object)$summary
+  }
+}
+
+#' @export
+print.report_priors <- function(x, ...) {
+  cat(paste0(x, collapse = "\n"))
+}
+
+

README.Rmd

@@ -18,7 +18,7 @@ editor_options:
 knitr::opts_chunk$set(
   collapse = TRUE,
   tidy.opts=list(width.cutoff=60),
-  tidy=TRUE,
+  tidy=FALSE,
   dpi=450,
   fig.path = "man/figures/"
 )
@@ -100,58 +100,185 @@ The `report()` function works on a variety of models, as well as other objects s
 report(iris)
 ```
 ```{r echo=FALSE, message=FALSE, warning=FALSE}
-print(report(iris), width = 120)
+print(report(iris), width = 110)
 ```
 
 These reports nicely work within the [*tidyverse*](https://github.com/tidyverse) workflow:
 
 ```{r message=FALSE, warning=FALSE, eval=FALSE}
 library(dplyr)
 
-cor.test(iris$Sepal.Length, iris$Petal.Length) %>% 
+iris %>%
+  select(-starts_with("Sepal")) %>% 
+  group_by(Species) %>% 
+  report() %>% 
+  summary()
+```
+```{r echo=FALSE, message=FALSE, warning=FALSE}
+iris %>%
+  select(-starts_with("Sepal")) %>% 
+  group_by(Species) %>% 
+  report() %>% 
+  summary() %>% 
+  print(width=110)
+```
+
+
+### *t*-tests and correlations
+
+Reports can be used to automatically format tests like *t*-tests or correlations.
+
+```{r message=FALSE, warning=FALSE, eval=FALSE}
+report(t.test(mtcars$mpg ~ mtcars$am))
+```
+```{r echo=FALSE, message=FALSE, warning=FALSE}
+t.test(mtcars$mpg ~ mtcars$am) %>%
+  report() %>%
+  print(width = 110)
+```
+
+As mentioned, you can also create tables with the `as.data.frame()` functions, like for example with this correlation test:
+
+```{r message=FALSE, warning=FALSE}
+cor.test(iris$Sepal.Length, iris$Sepal.Width) %>% 
+  report() %>% 
+  as.data.frame()
+```
+
+
+
+### ANOVAs
+
+This works great with ANOVAs, as it includes **effect sizes** and their interpretation.
+
+```{r message=FALSE, warning=FALSE, eval=FALSE}
+aov(Sepal.Length ~ Species, data=iris) %>% 
   report()
 ```
 ```{r echo=FALSE, message=FALSE, warning=FALSE}
-print(report(cor.test(iris$Sepal.Length, iris$Petal.Length)), width = 120)
+aov(Sepal.Length ~ Species, data=iris) %>%
+  report() %>%
+  print(width = 110)
 ```
 
-<!-- You can also create tables with the `table_short()` and `table_long()` functions: -->
+### General Linear Models (GLMs)
 
+Reports are also compatible with GLMs, such as this **logistic regression**:
+
+```{r message=FALSE, warning=FALSE, eval=FALSE}
+model <- glm(vs ~ mpg * drat, data=mtcars, family="binomial")
+
+report(model)
+```
+```{r echo=FALSE, message=FALSE, warning=FALSE}
+glm(vs ~ mpg * drat, data=mtcars, family="binomial") %>%
+  report() %>%
+  print(width = 110)
+```
 
-<!-- ```{r message=FALSE, warning=FALSE} -->
-<!-- lm(Sepal.Length ~ Petal.Length + Species, data = iris) %>%  -->
-<!--   report() %>%  -->
-<!--   table_short() -->
-<!-- ``` -->
+### Mixed Models
 
+Mixed models (coming from example from the `lme4` package), which popularity and usage is exploding, can also be reported as it should:
 
+```{r message=FALSE, warning=FALSE, eval=FALSE}
+library(lme4)
 
-<!-- ### *t*-tests and correlations -->
+model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
 
-<!-- ```{r message=FALSE, warning=FALSE, eval=FALSE} -->
-<!-- t.test(mtcars$mpg ~ mtcars$am) %>%  -->
-<!--   report() -->
-<!-- ``` -->
-<!-- ```{r echo=FALSE, message=FALSE, warning=FALSE} -->
-<!-- t.test(mtcars$mpg ~ mtcars$am) %>%  -->
-<!--   report() %>%  -->
-<!--   text_long() %>%  -->
-<!--   print(width = 100) -->
-<!-- ``` -->
+report(model)
+```
+```{r echo=FALSE, message=FALSE, warning=FALSE}
+library(lme4)
 
+lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris) %>%
+  report() %>%
+  print(width = 110)
+```
 
-<!-- ### Report participants details -->
 
-<!-- This can be useful to complete the **Participants** paragraph of your manuscript. -->
+### Bayesian Models
 
-<!-- ```{r message=FALSE, warning=FALSE} -->
-<!-- data <- data.frame("Age" = c(22, 23, 54, 21), -->
-<!--                    "Sex" = c("F", "F", "M", "M")) -->
+Bayesian models can also be reported using the new [**SEXIT**](https://easystats.github.io/bayestestR/reference/sexit.html) framework, that combines clarity, precision and usefulness.
 
-<!-- paste(report_participants(data, spell_n = TRUE), -->
-<!--       "were recruited in the study by means of torture and coercion.") -->
-<!-- ``` -->
+```{r message=FALSE, warning=FALSE, eval=FALSE}
+library(rstanarm)
 
+model <- stan_glm(mpg ~ qsec + wt, data = mtcars)
+
+report(model)
+```
+```{r echo=FALSE, message=FALSE, warning=FALSE}
+library(rstanarm)
+
+model <- stan_glm(mpg ~ qsec + wt, data = mtcars, refresh=0, iter=1000) %>%
+  report() %>%
+  print(width = 110)
+```
+
+## Other types of reports
+
+### Specific parts
+
+One can, for complex reports, directly access the pieces of the reports:
+
+```{r message=FALSE, warning=FALSE, eval=FALSE}
+model <- lm(Sepal.Length ~ Species, data = iris)
+
+report_model(model)
+report_performance(model)
+report_statistics(model)
+```
+```{r echo=FALSE, message=FALSE, warning=FALSE}
+model <- lm(Sepal.Length ~ Species, data = iris)
+
+print(report_model(model), width=110)
+print(report_performance(model), width=110)
+print(report_statistics(model), width=110)
+```
+
+
+### Report participants details
+
+This can be useful to complete the **Participants** paragraph of your manuscript.
+
+```{r message=FALSE, warning=FALSE, eval=FALSE}
+data <- data.frame("Age" = c(22, 23, 54, 21),
+                   "Sex" = c("F", "F", "M", "M"))
+
+paste(
+  report_participants(data, spell_n = TRUE),
+  "were recruited in the study by means of torture and coercion."
+  )
+```
+```{r echo=FALSE, message=FALSE, warning=FALSE}
+data <- data.frame("Age" = c(22, 23, 54, 21),
+                   "Sex" = c("F", "F", "M", "M"))
+
+paste(report_participants(data, spell_n = TRUE),
+      "were recruited in the study by means of torture and coercion.") %>% 
+  print(width=110)
+```
+
+
+### Report sample
+
+Report can also help you create sample description table (also referred to as **Table 1**).
+
+```{r message=FALSE, warning=FALSE}
+report_sample(iris, group_by = "Species")
+```
+
+### Report system and packages
+
+Finally, **report** includes some functions to help you write the data analysis paragraph about the tools used.
+
+```{r message=FALSE, warning=FALSE, eval=FALSE}
+report(sessionInfo())
+```
+```{r echo=FALSE, message=FALSE, warning=FALSE}
+report(sessionInfo()) %>% 
+  print(width=110)
+```
 
 ## Credits