plotmod: Moderation Effect Plot

In stdmod: Standardized Moderation Effect and Its Confidence Interval

Moderation Effect Plot

Description

Plot the moderation effect in a regression model

Usage

plotmod(
  output,
  x,
  w,
  x_label,
  w_label,
  y_label,
  title,
  digits = 3,
  x_from_mean_in_sd = 1,
  w_from_mean_in_sd = 1,
  w_method = c("sd", "percentile"),
  w_percentiles = c(0.16, 0.84),
  x_method = c("sd", "percentile"),
  x_percentiles = c(0.16, 0.84),
  w_sd_to_percentiles = NA,
  x_sd_to_percentiles = NA,
  w_values = NULL,
  note_standardized = TRUE,
  no_title = FALSE,
  line_width = 1,
  point_size = 5,
  graph_type = c("default", "tumble")
)

Arguments

output	The output of stats::lm(), std_selected(), or std_selected_boot().
x	The name of the focal variable (x-axis) in 'output“. It can be the name of the variable, with or without quotes. Currently only numeric variables are supported.
w	The name of the moderator in output. It can be the name of the variable, with or without quotes.
x_label	The label for the X-axis. Default is the value of x.
w_label	The label for the legend for the lines. Default is the value ofw.
y_label	The label for the Y-axis. Default is the name of the response variable in the model.
title	The title of the graph. If not supplied, it will be generated from the variable names or labels (in x_label, y_label, and w_label). If "", no title will be printed. This can be used when the plot is for manuscript submission and figures are required to have no titles.
digits	Number of decimal places to print. Default is 3.
x_from_mean_in_sd	How many SD from mean is used to define "low" and "high" for the focal variable. Default is 1.
w_from_mean_in_sd	How many SD from mean is used to define "low" and "high" for the moderator. Default is 1. Ignored if w is categorical.
w_method	How to define "high" and "low" for the moderator levels. Default is in terms of the standard deviation of the moderator, "sd". If equal to "percentile", then the percentiles of the moderator in the dataset are used. Ignored if w is categorical.
w_percentiles	If w_method is "percentile", then this argument specifies the two percentiles to be used, divided by 100. It must be a vector of two numbers. The default is c(.16, .84), the 16th and 84th percentiles, which corresponds approximately to one SD below and above mean for a normal distribution, respectively. Ignored if w is categorical.
x_method	How to define "high" and "low" for the focal variable levels. Default is in terms of the standard deviation of the focal variable, "sd". If equal to "percentile", then the percentiles of the focal variable in the dataset is used.
x_percentiles	If x_method is "percentile", then this argument specifies the two percentiles to be used, divided by 100. It must be a vector of two numbers. The default is c(.16, .84), the 16th and 84th percentiles, which corresponds approximately to one SD below and above mean for a normal distribution, respectively.
w_sd_to_percentiles	If w_method is "percentile" and this argument is set to a number, this number will be used to determine the percentiles to be used. The lower percentile is the percentile in a normal distribution that is w_sd_to_percentiles SD below the mean. The upper percentile is the percentile in a normal distribution that is w_sd_to_percentiles SD above the mean. Therefore, if w_sd_to_percentiles is set to 1, then the lower and upper percentiles are 16th and 84th, respectively. Default is NA.
x_sd_to_percentiles	If x_method is "percentile" and this argument is set to a number, this number will be used to determine the percentiles to be used. The lower percentile is the percentile in a normal distribution that is x_sd_to_percentiles SD below the mean. The upper percentile is the percentile in a normal distribution that is x_sd_to_percentiles SD above the mean. Therefore, if x_sd_to_percentiles is set to 1, then the lower and upper percentiles are 16th and 84th, respectively. Default is NA.
w_values	The values of w to be used. Default is NULL. If a numeric vector is supplied, these values will be used to compute the conditional effects. Other arguments on generating levels are ignored. Note that, if w has been standardized or centered, these values are for the standardized or centered w. The values will always be sorted. This argument is ignored if w is categorical.
note_standardized	If TRUE, will check whether a variable has SD nearly equal to one. If yes, will report this in the plot. Default is TRUE.
no_title	If TRUE, title will be suppressed. Default is FALSE.
line_width	The width of the lines as used in ggplot2::geom_segment(). Default is 1.
point_size	The size of the points as used in ggplot2::geom_point(). Default is 5.
graph_type	If "default", the typical line-graph with equal end-points will be plotted. If "tubmle", then the tumble graph proposed by Bodner (2016) will be plotted. Default is "default".

Details

This function generate a basic ggplot2 graph typically found in psychology manuscripts. It tries to check whether one or more variables are standardized, and report this in the plot if required.

This function only has features for typical plots of moderation effects. It is not intended to be a flexible tool for a fine control on the plots.

Value

A ggplot2 graph. Plotted if not assigned to a name. It can be further modified like a usual ggplot2 graph.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

References

Bodner, T. E. (2016). Tumble graphs: Avoiding misleading end point extrapolation when graphing interactions from a moderated multiple regression analysis. Journal of Educational and Behavioral Statistics, 41(6), 593-604. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3102/1076998616657080")}

Examples

# Do a moderated regression by lm
lm_out <- lm(sleep_duration ~ age + gender + emotional_stability*conscientiousness, sleep_emo_con)
plotmod(lm_out,
        x = emotional_stability,
        w = conscientiousness,
        x_label = "Emotional Stability",
        w_label = "Conscientiousness",
        y_label = "Sleep Duration")

# Standardize all variables except for categorical variables
# Alternative: use to_standardize as a shortcut
# lm_std <- std_selected(lm_out,
#                        to_standardize = ~ .)
lm_std <- std_selected(lm_out,
                       to_scale = ~ .,
                       to_center = ~ .)
plotmod(lm_std,
        x = emotional_stability,
        w = conscientiousness,
        x_label = "Emotional Stability",
        w_label = "Conscientiousness",
        y_label = "Sleep Duration")

# Tumble Graph
plotmod(lm_std,
        x = emotional_stability,
        w = conscientiousness,
        x_label = "Emotional Stability",
        w_label = "Conscientiousness",
        y_label = "Sleep Duration",
        graph_type = "tumble")

stdmod documentation built on Sept. 30, 2024, 9:42 a.m.