Add simple vignette for tinyplot

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: modelbased
 Title: Estimation of Model-Based Predictions, Contrasts and Means
-Version: 0.13.0.7
+Version: 0.13.0.8
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",

R/options.R

@@ -32,6 +32,10 @@
 #' - `options(modelbased_full_labels = FALSE)` will remove redundant
 #'   (duplicated) labels from rows.
 #'
+#' - `options(easystats_display_format = <value>)` will set the default format
+#'   for the `display()` methods. Can be one of `"markdown"`, `"html"`, or
+#'   `"tt"`. See [`display.estimate_contrasts()`] for details.
+#'
 #' **For plotting**
 #'
 #' - `options(modelbased_join_dots = <logical>)` will set a default value for
@@ -45,7 +49,7 @@
 #'   the `alpha` argument of the `ribbon` geom. Should be a number between `0`
 #'   and `1`.
 #'
-#' - `options(easystats_display_format = <value>)` will set the default format
-#'   for the `display()` methods. Can be one of `"markdown"`, `"html"`, or
-#'   `"tt"`. See [`display.estimate_contrasts()`] for details.
+#' - `options(modelbased_tinyplot_dodge = <number>)` will set a default value
+#'   for the `dodge` argument (spacing between geoms) when using
+#'   `tinyplot::plt()`. Should be a number between `0` and `1`.
 NULL

R/tinyplot.R

@@ -45,7 +45,7 @@
   # now need to extract the aesthetics and data and use it to create a tinyplot
   # object
   aes <- .find_aes(x, model_info, numeric_as_discrete)
   data <- aes$data
   aes <- aes$aes
 
   # save additional arguments, once for theming and once for the plot
@@ -63,7 +63,7 @@
   }
 
   # Don't plot raw data for transformed responses with no back-transformation
   transform <- attributes(x)$transform
 
   if (isTRUE(model_info$is_linear) && !isTRUE(transform)) {
     # add information about response transformation
@@ -107,8 +107,12 @@
 
   # dodging -------------------------------
 
+  # Set dodge value for grouped point or pointrange plots.
+  # The value 0.07 was chosen to reduce overlap in this context; adjust via
+  # option if needed.
+  dodge_value <- getOption("modelbased_tinyplot_dodge", 0.07)
   if (!is.null(aes$color) && aes$type %in% c("pointrange", "point")) {
-    dots$dodge <- 0.1
+    dots$dodge <- dodge_value
   }
 
   ## TODO: legend labels?
@@ -144,7 +148,7 @@
     }
 
     # add layer
     plot_args$draw <- {
       tinyplot::tinyplot(
         # we need the original response name for the data points
         # so we update the formula for the plot description
@@ -162,7 +166,7 @@
   }
 
   # plot it!
-  do.call(tinyplot::tinyplot, plot_args)
+  suppressWarnings(do.call(tinyplot::tinyplot, plot_args))
 }
 
 #' @exportS3Method tinyplot::tinyplot

R/visualisation_recipe.R

@@ -70,7 +70,11 @@
 #'   will set a default value for the `alpha` argument of the `ribbon` geom.
 #'   Should be a number between `0` and `1`.
 #'
+#' - `modelbased_tinyplot_dodge`: `options(modelbased_tinyplot_dodge = <number>)`
+#'   will set a default value for the `dodge` argument (spacing between geoms)
+#'   when using `tinyplot::plt()`. Should be a number between `0` and `1`.
+#'
 #' @examplesIf all(insight::check_if_installed(c("marginaleffects", "see", "ggplot2"), quietly = TRUE)) && getRversion() >= "4.1.0"
 #' library(ggplot2)
 #' library(see)
 #' # ==============================================

pkgdown/_pkgdown.yml

@@ -69,6 +69,8 @@ navbar:
         - text: "Visualization"
         - text: "Plotting estimated marginal means"
           href: articles/plotting.html
+        - text: "Plotting estimated marginal means with tinyplot"
+          href: articles/plotting_tinyplot.html
         - text: "Visualize effects and interactions"
           href: articles/estimate_relation.html
         - text: "The modelisation approach"

vignettes/overview_of_vignettes.Rmd

@@ -47,6 +47,7 @@ All package vignettes are available at [https://easystats.github.io/modelbased/]
 ### Visualization
 
 * [Plotting estimated marginal means](https://easystats.github.io/modelbased/articles/plotting.html)
+* [Plotting estimated marginal means with tinyplot](https://easystats.github.io/modelbased/articles/plotting_tinyplot.html)
 * [Visualize effects and interactions](https://easystats.github.io/modelbased/articles/estimate_relation.html)
 * [The Modelisation Approach to Statistics](https://easystats.github.io/modelbased/articles/modelisation_approach.html)
 

vignettes/plotting_tinyplot.Rmd

@@ -0,0 +1,177 @@
+---
+title: "Plotting estimated marginal means with tinyplot"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Plotting estimated marginal means with tinyplot}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+bibliography: bibliography.bib
+---
+
+```{r set-options, echo = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  dev = "png",
+  out.width = "100%",
+  fig.width = 7,
+  fig.height = 4,
+  dpi = 300,
+  message = FALSE,
+  warning = FALSE,
+  package.startup.message = FALSE
+)
+
+options(modelbased_join_dots = FALSE)
+options(modelbased_select = "minimal")
+
+pkgs <- c("marginaleffects", "tinyplot")
+if (!all(insight::check_if_installed(pkgs, quietly = TRUE))) {
+  knitr::opts_chunk$set(eval = FALSE)
+}
+if (getRversion() < "4.1.0") {
+  knitr::opts_chunk$set(eval = FALSE)
+}
+```
+
+This vignette provides a quick overview with different examples that show how to plot estimated marginal means, like [in this vignette](https://easystats.github.io/modelbased/articles/plotting.html), however, here we use the [`{tinyplot}`](https://grantmcdermott.com/tinyplot/) package instead of `{ggplot2}` to create the plots.
+
+## One predictor - categorical
+
+The simplest case is possibly plotting one categorical predictor. Predicted values for each level and its confidence intervals are shown.
+
+```{r}
+library(modelbased)
+library(tinyplot)
+
+data(efc, package = "modelbased")
+efc <- datawizard::to_factor(efc, c("e16sex", "c172code", "e42dep"))
+
+m <- lm(neg_c_7 ~ e16sex + c172code + barthtot, data = efc)
+estimate_means(m, "c172code") |> plt()
+```
+
+## One predictor - numeric
+
+For numeric predictors, the range of predictions at different values of the focal predictor are plotted, the uncertainty is displayed as confidence band.
+
+```{r}
+estimate_means(m, "barthtot") |> plt()
+```
+
+## Two predictors - categorical
+
+For two categorical predictors, the first focal predictors is plotted along the x-axis, while the levels of the second predictor are mapped to different colors.
+
+```{r}
+m <- lm(neg_c_7 ~ e16sex * c172code + e42dep, data = efc)
+estimate_means(m, c("e16sex", "c172code")) |> plt()
+```
+
+## Two predictors - numeric * categorical
+
+For two predictors, where the first is numeric and the second categorical, range of predictions including confidence bands are shown, with the different levels of the second (categorical) predictor mapped to colors again.
+
+```{r}
+m <- lm(neg_c_7 ~ barthtot * c172code + e42dep, data = efc)
+estimate_means(m, c("barthtot", "c172code")) |> plt()
+```
+
+In general, plots can be further modified using functions or arguments from the **tinyplot** package. Thereby, other themes, color scales, faceting and so on, can be applied.
+
+```{r}
+estimate_means(m, c("barthtot", "c172code")) |>
+  plt(facet = ~c172code)
+
+estimate_means(m, c("barthtot", "c172code")) |>
+  plt(palette = "okabe")
+```
+
+## Two predictors - categorical * numeric
+
+If the numeric predictor is the _second_ focal term, its values are still mapped to colors, however, by default to a continuous (gradient) scale, because a range of representative values for that numeric predictor is used by default.
+
+Focal predictors specified in `estimate_means()` are passed to `insight::get_datagrid()`. If not specified otherwise, representative values for numeric predictors are evenly distributed from the minimum to the maximum, with a total number of `length` values covering that range.
+
+I.e., by default, arguments `range = "range"` and `length = 10` in `insight::get_datagrid()`, and thus for numeric predictors, a _range_ of _length_ values is used to estimate predictions.
+
+```{r}
+# by default, `range = "range"` and `length = 10`
+estimate_means(m, c("c172code", "barthtot")) |> plt()
+```
+
+That means that the `length` argument can be used to control how many values (lines) for the numeric predictors are chosen.
+
+```{r}
+estimate_means(m, c("c172code", "barthtot"), length = 20) |> plt()
+```
+
+Another option would be to use `range = "grid"`, in which case the mean and +/- one standard deviation around the mean are chosen as representative values for numeric predictors.
+
+```{r}
+estimate_means(m, c("c172code", "barthtot"), range = "grid") |> plt()
+```
+
+It is also possible to specify representative values, at which the estimated marginal means of the outcome should be plotted. Again, consult the documentation at `?insight::get_datagrid` for further details.
+
+```{r}
+estimate_means(
+  m,
+  c(
+    "c172code = c('low level of education', 'high level of education')",
+    "barthtot = c(30, 50, 80)"
+  )
+) |> plt()
+
+estimate_means(m, c("c172code", "barthtot = [fivenum]")) |> plt()
+```
+
+## Three numeric predictors
+
+The default plot-setting for three numeric predictors can be rather confusing.
+
+```{r}
+m <- lm(neg_c_7 ~ c12hour * barthtot * c160age, data = efc)
+estimate_means(m, c("c12hour", "barthtot", "c160age")) |> plt()
+```
+
+Instead, it is recommended to use `length`, create a "reference grid", or again specify meaningful values directly in the `by` argument.
+
+```{r}
+estimate_means(m, c("c12hour", "barthtot", "c160age"), length = 2) |> plt()
+
+estimate_means(m, c("c12hour", "barthtot", "c160age"), range = "grid") |> plt()
+```
+
+## Three categorical predictors
+
+Multiple categorical predictors are usually less problematic, since discrete color scales and faceting are used to distinguish between factor levels.
+
+```{r}
+m <- lm(neg_c_7 ~ e16sex * c172code * e42dep, data = efc)
+estimate_means(m, c("e16sex", "c172code", "e42dep")) |> plt()
+```
+
+## Smooth plots
+
+Remember that by default a range of ten values is chosen for numeric focal predictors. While this mostly works well for plotting linear relationships, plots may look less smooth for certain models that involve quadratic or cubic terms, or splines, or for instance if you have GAMs.
+
+```{r}
+m <- lm(neg_c_7 ~ e16sex * c12hour + e16sex * I(c12hour^2), data = efc)
+estimate_means(m, c("c12hour", "e16sex")) |> plt()
+```
+
+In this case, simply increase the number of representative values by setting `length` to a higher number.
+
+```{r}
+estimate_means(m, c("c12hour", "e16sex"), length = 200) |> plt()
+```
+
+```{r echo=FALSE}
+# reset options
+options(
+  modelbased_join_dots = NULL,
+  modelbased_estimate = NULL,
+  modelbased_select = NULL
+)
+```