actually add the vignette...

R/plot_interaction.R

@@ -62,8 +62,9 @@ plot_interaction <- function(x, z, y, xz = NULL, vals_x = seq(-3, 3, .001),
                              ci_width = 1.96, rescale = TRUE, ...) {
   df <- simple_slopes(x = x, z = z, y = y, model = model, vals_x = vals_x, vals_z = vals_z, 
                       rescale = rescale, ci_width = ci_width, ...)
-  df$cat_z     <- as.factor(round(df$vals_z, digits))
-
+  df$cat_z <- as.factor(round(df$vals_z, digits))
+
+  # declare within the scope, to not get notes in R CMD check
   std.error <- df$std.error
   predicted <- df$predicted
   cat_z     <- df$cat_z
@@ -93,6 +94,7 @@ calc_se <- function(x, var, n, s) {
   s * sqrt(1 / n + x ^ 2 / SSx)
 }
 
+
 #' Plot Interaction Effect Using the Johnson-Neyman Technique
 #'
 #' This function plots the simple slopes of an interaction effect across different values of a moderator variable using the Johnson-Neyman technique. It identifies regions where the effect of the predictor on the outcome is statistically significant.

vignettes/simple_slopes.Rmd

@@ -0,0 +1,92 @@
+---
+title: "simple slopes analysis"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{simple slopes analysis}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(modsem)
+```
+
+# Simple Slopes Analysis
+
+Simple slope effects can be plotted using the included `plot_interaction()` function. This function takes a fitted model object and the names of the two variables that are interacting. The function will plot the interaction effect of the two variables, where:
+
+- The x-variable is plotted on the x-axis.
+- The y-variable is plotted on the y-axis.
+- The z-variable determines at which points the effect of x on y is plotted.
+
+The function will also plot the 95% confidence interval for the interaction effect. 
+Note that the `vals_z` argument (as well as the values of `x`) are scaled by the
+mean and standard deviation of the variables. Unless the `rescale` argument is set to `FALSE`.
+
+Here is a simple example using the double-centering approach:
+
+```{r}
+m1 <- "
+# Outer Model
+  X =~ x1
+  X =~ x2 + x3
+  Z =~ z1 + z2 + z3
+  Y =~ y1 + y2 + y3
+
+# Inner Model
+  Y ~ X + Z + X:Z
+"
+est1 <- modsem(m1, data = oneInt)
+plot_interaction(x = "X", z = "Z", y = "Y", vals_z = c(0, 1), model = est1)
+```
+
+If you want to see the numerical values of the simple slopes, you can use the `simple_slopes()` function:
+
+```{r}
+m1 <- "
+# Outer Model
+  X =~ x1
+  X =~ x2 + x3
+  Z =~ z1 + z2 + z3
+  Y =~ y1 + y2 + y3
+
+# Inner Model
+  Y ~ X + Z + X:Z
+"
+
+est1 <- modsem(m1, data = oneInt)
+simple_slopes(x = "X", z = "Z", y = "Y", vals_z = c(0, 1), model = est1)
+```
+
+The `simple_slopes()` function returns a simple_slopes object, which is a `data.frame`
+with some additional attributes. It only has a single method (or technically, a generic function),
+`print.simple_slopes()`, which prints the simple slopes in a easy-to-read format. If you want to 
+extract the simple slopes as a `data.frame`, you can use the `as.data.frame()` function:
+
+
+```{r}
+m1 <- "
+# Outer Model
+  X =~ x1
+  X =~ x2 + x3
+  Z =~ z1 + z2 + z3
+  Y =~ y1 + y2 + y3
+
+# Inner Model
+  Y ~ X + Z + X:Z
+"
+
+est1 <- modsem(m1, data = oneInt)
+slopes <- simple_slopes(x = "X", z = "Z", y = "Y", 
+                        vals_z = c(0, 1), model = est1)
+as.data.frame(slopes)
+```
+
+