---
title: "Standardized Moderation Effect by std_selected()"
author: "Shu Fai Cheung and David Weng Ngai Vong"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Standardized Moderation Effect by std_selected()}
%\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"
)
```
# Purpose
This document demonstrates how to use `std_selected()` from
the `stdmod` package to compute the correct
standardized solution of moderated regression.
More about this package can be found
in `vignette("stdmod", package = "stdmod")`
or at [https://sfcheung.github.io/stdmod/](https://sfcheung.github.io/stdmod/).
# Setup the Environment
```{r setup}
library(stdmod) # For computing the standardized moderation effect conveniently
```
# Load the Dataset
```{r load_dataset}
data(sleep_emo_con)
head(sleep_emo_con, 3)
```
This data set has 500 cases of data. The variables are sleep duration, age, gender,
and the scores from two personality scales, emotional stability and
conscientiousness of the IPIP Big Five markers. Please refer to
(citation to be added) for the detail of the data set.
The names of some variables are shortened for readability:
```{r}
colnames(sleep_emo_con)[3:4] <- c("cons", "emot")
head(sleep_emo_con, 3)
```
# Moderated Regression
Suppose we are interested in predicting sleep duration by emotional stability,
after controlling for gender and age. However, we suspect that the effect of
emotional stability, if any, may be moderated by conscientiousness. Therefore,
we conduct a moderated regression as follow:
```{r mod_reg}
lm_out <- lm(sleep_duration ~ age + gender + emot * cons,
data = sleep_emo_con)
summary(lm_out)
plotmod(lm_out,
x = "emot",
w = "cons",
x_label = "Emotional Stability",
w_label = "Conscientiousness",
y_label = "Sleep Duration")
```
The results show that conscientiousness significantly moderates the effect of
emotional stability on sleep duration.
# Standardized Moderation Effect
To get the correct standardized solution of the moderated regression, with the
product term formed *after* standardization, we can use `std_selected()`.
- The first argument is the regression output from `lm()`.
- The argument `to_center` specifies variables to be mean
centered.
- The argument `to_scale` specifies variables to be rescaled
by their standard deviations after centering.
- In `stdmod` 0.2.6.3, the argument `to_standardize` was introduced
as a shortcut. Listing a variable in `to_standardize` is
equivalent to listing it in `to_center` and `to_scale`.
If we want to standardize or mean center all variables, we can use `~ .` as a
shortcut. Note that `std_selected()` will automatically skip categorical
variables (i.e., factors or string variables in the regression model of `lm()`).
```{r}
lm_stdall <- std_selected(lm_out,
to_standardize = ~ .)
```
Before 0.2.6.3, to standardize all variables except for
categorical variables, we need to use both `to_center = ~ .`
and `to_scale = ~ .`. Since 0.2.6.3,
we can just use `to_standardize = ~ .`, as shown above.
If `to_standardize = ~ .` does not work, just use
`to_center` and `to_scale` as shown below:
```r
lm_stdall <- std_selected(lm_out,
to_center = ~ .,
to_scale = ~ .)
```
A summary of the results of `std_selected()` can be
generated by `summary()`:
```{r}
summary(lm_stdall)
```
The coefficient in this solution,
`r round(coef(lm_stdall)["emot:cons"], 5)`,
can be interpreted as the change in the standardized effect of
emotional stability for each one standard deviation increase of
conscientiousness. Naturally, this can be called the
*standardized moderation effect* of conscientiousness
([Cheung, Cheung, Lau, Hui, & Vong, 2022](https://doi.org/10.1037/hea0001188)).
The output of `std_selected()` can be passed to other functions that accept the
output of `lm()`. This package also has a simple function,
`plotmod()`, for generating a typical plot of the moderation effect:
```{r mod_reg_stdall}
plotmod(lm_stdall,
x = "emot",
w = "cons",
x_label = "Emotional Stability",
w_label = "Conscientiousness",
y_label = "Sleep Duration")
```
The function `plotmod()` also prints the conditional effects of the predictor
(focal variable), emotional stability in this example.
# The Common (Incorrect) Standardized Solution
For comparison, this is the results of standardizing all variables, including
the product term and the categorical variable.
```{r}
library(lm.beta) # For generating the typical standardized solution
packageVersion("lm.beta")
lm_beta <- lm.beta(lm_out)
summary(lm_beta)
```
The coefficient of the *standardized* product term is
`r round(coef(lm_beta)["emot:cons"], 5)`, which
*cannot* be interpreted as the change in the standardized effect of
emotional stability for each one standard deviation increase of
conscientiousness because the product term is standardized and can no longer
be interpreted as the product of two variables in the model.
# Improved Confidence Intervals
It has been shown (e.g., [Yuan & Chan, 2011](https://doi.org/10.1007/s11336-011-9224-6))
that the standard errors of
standardized regression coefficients computed just by standardizing the variables
are biased, and consequently the confidence intervals are also invalid. The
function `std_selected_boot()` is a wrapper of `std_selected()` that also
forms the confidence interval of the regression coefficients when standardizing
is conducted, using nonparametric bootstrapping as suggested by
Cheung, Cheung, Lau, Hui, and Vong (2022).
We use the same example above that standardizes all variables except for
categorical variables to illustrate this function. The argument `nboot`
specifies the number of nonparametric bootstrap samples.
The level of confidence is set by `conf`. The default is .95, denoting 95%
confidence intervals. If this is the desired level, this argument can be
omitted.
```{r echo = FALSE, eval = TRUE}
if (file.exists("eg2_lm_xwy_std_ci.rds")) {
lm_xwy_std_ci <- readRDS("eg2_lm_xwy_std_ci.rds")
} else {
set.seed(649017)
lm_xwy_std_ci <- std_selected_boot(lm_out, to_center = ~ .,
to_scale = ~ .,
nboot = 2000)
saveRDS(lm_xwy_std_ci, "eg2_lm_xwy_std_ci.rds", compress = "xz")
}
```
```r
set.seed(649017)
lm_xwy_std_ci <- std_selected_boot(lm_out,
to_standardize = ~ .,
nboot = 2000)
```
If the default options are acceptable, the only additional argument is `nboot`.
```{r}
summary(lm_xwy_std_ci)
```
```{r echo = FALSE}
tmp <- summary(lm_xwy_std_ci)$coefficients
```
The standardized moderation effect is
`r formatC(tmp["emot:cons", "Estimate"], 4, format = "f")`,
and the 95% nonparametric bootstrap confidence interval is
`r formatC(tmp["emot:cons", "CI Lower"], 4, format = "f")` to
`r formatC(tmp["emot:cons", "CI Upper"], 4, format = "f")`.
Note: As a side product, the nonparametric bootstrap percentile confidence of the other
coefficients are also reported. They can be used for other variables that are
standardized in the same model, whether they are involved in the moderation or not.
# Further Information
`vignette("plotmod", package = "stdmod")` illustrates how to use `plotmod()` to plot a moderation
effect. If variables are standardized by `std_selected()`, `plotmod()` can
indicate this in the plot.
`vignette("cond_effect", package = "stdmod")` illustrates how to use `cond_effect()` to compute
conditional effects, the effect of a predictor (focal variable) for selected
levels of the moderator.
`cond_effect()` supports outputs from `std_selected()`.
# Reference(s)
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.
Yuan, K.-H., & Chan, W. (2011). Biases and standard errors of standardized
regression coefficients. *Psychometrika, 76*(4), 670-690.
https://doi.org/10.1007/s11336-011-9224-6