---
title: "Moderation Effects Plots by plotmod()"
author: "Shu Fai Cheung"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Moderation Effects Plots by plotmod()}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 6,
fig.height = 4,
fig.align = "center"
)
```
# Introduction
This vignette illustrates how to use
`plotmod()` from the `stdmod` package.
More about this package can be found
in `vignette("stdmod", package = "stdmod")`
or at [https://sfcheung.github.io/stdmod/](https://sfcheung.github.io/stdmod/).
# What `plotmod()` Can Do
It can visualize a moderation effect by plotting the effect of
the predictor (focal variable) on the outcome variable (dependent variable)
for different levels of
the moderator:
```{r plotmod_example, echo = FALSE}
library(stdmod)
data(sleep_emo_con)
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness",
x_label = "Emotional Stability",
w_label = "Conscientiousness",
y_label = "Sleep Duration")
```
It can also plot a tumble graph (Bodner, 2016), in which the width
of each line depends on the distribution of the predictor (focal variable)
in each level
of the moderator:
```{r plotmod_example2, echo = FALSE}
plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness",
x_label = "Emotional Stability",
w_label = "Conscientiousness",
y_label = "Sleep Duration",
graph_type = "tumble")
```
`plotmod()` is not designed to be a versatile tool. It is designed to
be a function "good-enough" for common scenarios. The output of
`plotmod()` is a `ggplot` object and so can be further manipulated
by functions from the `ggplot2` package.
# Major Arguments
## Model, Predictor (`x`), and Moderator (`w`)
- `output`: The output of `lm()`, `std_selected()`, or `std_selected_boot()`,
with at least one interaction term.
- `x`: The predictor (focal variable). The variable in the horizontal axis.
- `w`: The moderator. The variable for which the conditional effects will
be plotted.
These are the only required arguments. Just setting them can generate the
graph:
```{r plotmod_example_raw}
library(stdmod)
data(sleep_emo_con)
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness")
```
## Levels of the Moderator
### Numeric Moderators
If the moderator is a numeric variable, then two lines will
be drawn, by default. One line for one standard deviation (SD) below the mean
on the
moderator ("Low"), and the other for one SD above the mean ("High").
Users can also use percentiles to define "Low" and "High" by setting
`w_method` to `"percentile"`. By default, 16th percentile and
84th percentile are used, which correspond approximately to one SD below
and above mean, respectively, for a normal distribution.
```{r plotmod_example_raw_numeric}
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness",
w_method = "percentile")
```
### Categorical Moderators
If the moderator is a categorical variable (a string variable or
a factor), then one line will be drawn for each category.
```{r plotmod_example_raw_cat}
set.seed(61452)
sleep_emo_con$city <- sample(c("Alpha", "Beta", "Gamma"),
nrow(sleep_emo_con),
replace = TRUE)
lm_cat <- lm(sleep_duration ~ age + gender +
emotional_stability * city,
sleep_emo_con)
plotmod(lm_cat,
x = "emotional_stability",
w = "city")
```
## Tumble Graph
By default, each line starts from one SD below mean of the predictor to
one SD above mean of the predictor. This assumes that variation of the
predictor is the same for different levels of the moderator. However,
this may not be the case when the predictor and the moderator are associated.
Bodner (2016) proposed a type of graph called tumble graph that can show
the association between the predictor and the moderator, if any.
The idea is simple. Instead of using the SD of the predictor in the whole
sample, the mean and SD of the predictor for the selected level of the
moderator is used. (See the technical notes below on how it is implemented
in `plotmod()`.)
To plot a tumble graph, just set `graph_type` to `"tumble"`:
```{r plotmod_tumble}
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness",
graph_type = "tumble")
```
The graph above shows that, the distribution of the predictor shifts to
the left when the moderator is low.
# Decoration and Annotation
## Variable Labels
By default, the variable names are used in the graph. Users can supply
labels that will be used instead of variable names by setting
`x_label`, `w_label`, and `y_label` to strings for the predictor,
moderator, and outcome variable, respectively.
```{r plotmod_annotate}
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness",
x_label = "EMO",
w_label = "CON",
y_label = "SLEEP")
```
## Title
The default title is "Moderation Effect". This can be changed via the
argument `title`:
```{r plotmod_title}
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness",
title = "EMO Effects For Low/High CON")
```
Some journals require submitted figures to have no title because they will
be described by figure captions, supplied separately. The title can be
disabled by setting `no_title` to `TRUE`.
## Line Width and Point Size
`plotmod()` supports basic control of the lines. The width of the lines
can be set by `line_width` and the size on the end-points can be set
by `point_size`:
```{r plotmod_line_width}
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness",
point_size = 8,
line_width = 2)
```
# Information
## Conditional Effects
By default, `plotmod()` prints the conditional effects of the predictor
as a subtitle. This can be disabled by adding a `ggplot2::theme()` call
to the output and set `plot.subtitle` to `element_blank()`, which removes
the subtitle:
```{r plotmod_cond}
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
p <- plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness")
library(ggplot2)
p + theme(plot.subtitle = element_blank())
```
## Definitions of the Levels of the Moderator
By default, the definitions of the levels of moderator (e.g.,
one SD below mean for "Low" and one SD above mean for "High")
are printed as a caption. This can also be disabled by adding
a `ggplot2::theme()` call and set `plot.caption` to `element_blank()`:
```{r plotmod_levels}
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
p <- plotmod(lm_raw,
x = "emotional_stability",
w = "conscientiousness")
p + theme(plot.caption = element_blank())
```
## Any Variables Standardized?
If the output is generated by `std_selected()` or `std_selected_boot()`,
`plotmod()` will try to detect if a variable is standardized or not,
and report this in the graph:
```{r plotmod_any_std}
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
lm_std <- std_selected(lm_raw,
to_standardize = ~ emotional_stability + conscientiousness)
# Before Version 0.2.6.3 of stdmod, to_center and to_scale need to be used:
# lm_std <- std_selected(lm_raw,
# to_center = ~ emotional_stability + conscientiousness,
# to_scale = ~ emotional_stability + conscientiousness)
```
The output of `std_selected()` can then be passed to `plotmod()`:
```{r plotmod_std_selected}
plotmod(lm_std,
x = "emotional_stability",
w = "conscientiousness")
```
If the predictor, moderator, and outcome variable are all standardized,
then the moderation effect is standardized moderation effect
([Cheung, Cheung, Lau, Hui, & Vong, 2022](https://doi.org/10.1037/hea0001188)), and the
conditional effect printed on the graph are the standardized effects
of the predictor for different levels of the moderator.
If which variables are standardized will be reported in the main text
and so the figure does not need to report this, set `note_standardized`
to `FALSE`.
# Tweak the Graph
The output of `plotmod()` is a `ggplot` object. Therefore, it can be manipulated
by functions that modify a `ggplot` object. For example, users can change
the colors of the lines and the theme:
```{r plotmod_tweak}
lm_raw <- lm(sleep_duration ~ age + gender +
emotional_stability * conscientiousness,
sleep_emo_con)
p <- plotmod(lm_std,
x = "emotional_stability",
w = "conscientiousness")
p + scale_color_manual(values = c("blue", "red")) +
theme_classic()
```
# Technical Notes for Tumble Graph
Bodner (2016) proposed regressing the predictor on the moderator. `plotmod()`
uses a nonparametric approach.
If the moderator is numerical, for a level of moderator, say, one SD
below mean, a certain percentage of cases (about 16% of cases above
and below this value of the moderator) is used to compute the mean
and SD of the predictor, which will be used to determine the end-points
of the line segment for this level.
If the moderator is categorical, then the mean and SD of all cases
in each group will be used to the determine the end-points of a line
segment.
# Further Information
Please refer to the help page of `plotmod()` for other options available.
# Reference
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. \doi{10.3102/1076998616657080}
Cheung, S. F., Cheung, S.-H., Lau, E. Y. Y., Hui, C. H., & Vong, W. N. (2022)
Improving an old way to measure moderation effect in standardized units.
*Health Psychology*, *41*(7), 502-505. https://doi.org/10.1037/hea0001188.