summary.mod2rm: Print and summary function for objects of class "mod2rm"

In mod2rm: Moderation Analysis for Two-Instance Repeated Measures Designs

Print and summary function for objects of class "mod2rm"

Description

Prints a summary of a list object of class "mod2rm", and (if requested) plot the results of the Johnson-Neyman procedure.

Usage

## S3 method for class 'mod2rm'
summary(object, ...)

Arguments

object	An object of class "mod2rm"
...	Additional parameters. "plotjn = TRUE" produces a ggplot for the Johnson-Neyman procedure, "plotstyle" can be set to "simple" or "points" (including data points in the plot)

Details

This function produces a summary for the results of an object of the type mod2rm, and can further be used to plot a graph showing the results of the JN procedure, if it is included in the mod2rm object. #' Results include number and name(s) of moderator(s), sample size, results of a paired t-test between both dependent variables, the results of the moderation analysis, conditional effects of the moderator on each of the dependent variables, conditional effects at values of the moderator, and the results of the Johnson-Neyman procedure (including critical values, proportion of the sample above/below these values, and conditional effects around the significance regions.

Value

Prints summary of the object, then returns NULL or (when requested) a ggplot2 object for the Johnson-Neyman plot

Examples

# Generate a dataset with a Johnson-Neyman (non-)significance region within the response range:

repeat{
  df = data.frame(out1 = runif(n = 100, min = 1, max = 9), 
                  out2 = runif(n = 100, min = 1, max = 9), 
                  w1 = runif(n = 100, min = 1, max = 9),  
                  w2 = runif(n = 100, min = 1, max = 9),
                  w3 = runif(n = 100, min = 1, max = 9))
  res = mod2rm(df, out1, out2, w1, jn = TRUE)
  if(res$res_jn_area["num_jn"] == 2 & res$res_jn_area["center_significant"] == FALSE)
    break
}

# Show summary including plot
summary.mod2rm(res, plotjn = TRUE, plotstyle = "simple")

# Multiple regression (3 moderators, additive)
res1 = mod2rm(df, out1, out2, w1, w2, w3, method = 1)
summary.mod2rm(res1)

# Multiple regression (2 moderators, multiplicative, manually defined conditional effects)
res2 = mod2rm(df, out1, out2, w1, w2, MOD1val = c(2,3,4), MOD2val = c(4,5), method = 2)
summary.mod2rm(res2)

mod2rm documentation built on June 30, 2022, 9:06 a.m.