A Quick Start Guide on Using std_selected()

In stdmod: Standardized Moderation Effect and Its Confidence Interval

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Introduction

This vignette illustrates how to use std_selected(), the main function from the stdmod package. More about this package can be found in vignette("stdmod", package = "stdmod") or at https://sfcheung.github.io/stdmod/.

This Guide Shows to use std_selected() to:

• get the correct standardized regression coefficients of a moderated regression model, and

• form the valid confidence intervals of the standardized regression coefficients using nonparametric bootstrapping that takes into account the sampling variation due to standardization.

Sample Dataset

library(stdmod)
dat <- sleep_emo_con
head(dat, 3)

This dataset has 500 cases, with sleep duration (measured in average hours), conscientiousness, emotional stability, age, and gender (a "female" and "male").

The names of some variables are shortened for readability:

colnames(dat)[2:4] <- c("sleep", "cons", "emot")
head(dat, 3)

Model

Suppose this is the moderated regression model:

• Dependent variable (Outcome Variable): sleep duration (sleep)

• Independent variable (Predictor / Focal Variable): emotional stability (emot)

• Moderator: conscientiousness (cons)

• Control variables: age and gender

lm() can be used to fit this model:

lm_out <- lm(sleep ~ age + gender + emot * cons,
             dat = dat)
summary(lm_out)

The unstandardized moderation effect is significant, B = r formatC(coef(lm_out)["emot:cons"], 4, format = "f"). For each one unit increase of conscientiousness score, the effect of emotional stability decreases by r formatC(-1 * coef(lm_out)["emot:cons"], 4, format = "f").

Correct Standardization For the Moderated Regression

Suppose we want to find the correct standardized solution for the moderated regression, that is, all variables except for categorical variables are standardized. In a moderated regression model, the product term should be formed after standardization.

Instead of doing the standardization ourselves before calling lm(), we can pass the lm() output to std_selected(), and use ~ . for the arguments to_scale and to_center.

lm_stdall <- std_selected(lm_out,
                          to_scale = ~ .,
                          to_center = ~ .)

Since 0.2.6.3, to_standardize can be used as a shortcut:

lm_stdall <- std_selected(lm_out,
                          to_standardize = ~ .)

summary(lm_stdall)

In this example, the coefficient of the product term, which naturally can be called the standardized moderation effect, is significant, B = r formatC(coef(lm_stdall)["emot:cons"], 4, format = "f"). For each one standard deviation increase of conscientiousness score, the standardized effect of emotional stability decreases by r formatC(-1 * coef(lm_stdall)["emot:cons"], 4, format = "f").

The Arguments

Standardization is equivalent to centering by mean and then scaling by (dividing by) standard deviation. The argument to_center specifies the variables to be centered by their means, and the argument to_scale specifies the variables to be scaled by their standard deviations. The formula interface of lm() is used in these two arguments, with the variables on the right hand side being the variables to be centered and/or scaled.

The "." on the right hand side represents all variables in the model, including the outcome variable (sleep duration in this example).

std_selected() will also skip categorical variables automatically skipped because standardizing them will make their coefficients not easy to interpret.

Since 0.2.6.3, to_standardize is added as a shortcut. Listing a variable on to_standardize is equivalent to listing this variable on both to_center and to_scale.

Advantage

Using std_selected minimizes impact on the workflow. Do regression as usual. Get the correct standardized coefficients only when we need to interpret them.

Nonparametric Bootstrap Confidence Intervals

There is one problem with standardized coefficients. The confidence intervals based on ordinary least squares (OLS) fitted to the standardized variables do not take into account the sampling variation of the sample means and standard deviations (Yuan & Chan, 2011). Cheung, Cheung, Lau, Hui, and Vong (2022) suggest using nonparametric bootstrapping, with standardization conducted in each bootstrap sample.

This can be done by std_selected_boot(), a wrapper of std_selected():

if (file.exists("stdmod_lm_stdall_boot.rds")) {
    lm_stdall_boot <- readRDS("stdmod_lm_stdall_boot.rds")
  } else {
    set.seed(870432)
    lm_stdall_boot <- std_selected_boot(lm_out,
                            to_scale = ~ .,
                            to_center = ~ .,
                            nboot = 5000)
    saveRDS(lm_stdall_boot, "stdmod_lm_stdall_boot.rds", compress = "xz")
  }

set.seed(870432)
lm_stdall_boot <- std_selected_boot(lm_out,
                        to_scale = ~ .,
                        to_center = ~ .,
                        nboot = 5000)

Since 0.2.6.3, to_standardize can be used as a shortcut:

lm_stdall_boot <- std_selected_boot(lm_out,
                        to_standardize = ~ .
                        nboot = 5000)

The minimum additional argument is nboot, the number of bootstrap samples.

summary(lm_stdall_boot)

The output is similar to that of std_selected(), with additional information on the bootstrapping process.

tmp <- summary(lm_stdall_boot)$coefficients

The 95% bootstrap percentile confidence interval of the standardized moderation effect is r formatC(tmp["emot:cons", "CI Lower"], 4, format = "f") to r formatC(tmp["emot:cons", "CI Upper"], 4, format = "f").

Standardize Independent Variable (Focal Variable) and Moderator

std_selected() and std_selected_boot() can also be used to standardize only selected variables. There are cases in which we do not want to standardize some continuous variables because they are measured on interpretable units, such as hours.

Suppose we want to standardize only emotional stability and conscientiousness, and do not standardize sleep duration. We just list emot and cons on to_center and to_scale:

lm_std1 <- std_selected(lm_out,
                        to_scale = ~ emot + cons,
                        to_center = ~ emot + cons)

Since 0.2.6.3, to_standardize can be used a shortuct:

lm_std1 <- std_selected(lm_out,
                        to_standardize = ~ emot + cons)

summary(lm_std1)

The partially standardized moderation effect is r formatC(coef(lm_std1)["emot:cons"], 4, format = "f"). For each one standard deviation increase of conscientiousness score, the partially standardized effect of emotional stability decreases by r formatC(-1 * coef(lm_std1)["emot:cons"], 4, format = "f").

Nonparametric Bootstrap Confidence Intervals

The function std_selected_boot() can also be used to form the nonparametric bootstrap confidence interval when only some of the variables are standardized:

if (file.exists("stdmod_lm_std1_boot.rds")) {
    lm_std1_boot <- readRDS("stdmod_lm_std1_boot.rds")
  } else {
    set.seed(870432)
    lm_std1_boot <- std_selected_boot(lm_out,
                            to_scale = ~ emot + cons,
                            to_center = ~ emot + cons,
                            nboot = 5000)
    saveRDS(lm_std1_boot, "stdmod_lm_std1_boot.rds", compress = "xz")
  }

set.seed(870432)
lm_std1_boot <- std_selected_boot(lm_out,
                        to_scale = ~ emot + cons,
                        to_center = ~ emot + cons,
                        nboot = 5000)

Since 0.2.6.3, to_standardize can be used as a shortcut:

lm_std1_boot <- std_selected_boot(lm_out,
                        to_standardize = ~ emot + cons,
                        nboot = 5000)

Again, the only additional argument is nboot.

summary(lm_std1_boot)

tmp <- summary(lm_std1_boot)$coefficients

The 95% bootstrap percentile confidence interval of the partially standardized moderation effect is r formatC(tmp["emot:cons", "CI Lower"], 4, format = "f") to r formatC(tmp["emot:cons", "CI Upper"], 4, format = "f").

Further Information

A more detailed illustration can be found at vignette("moderation", package = "stdmod").

vignette("std_selected", package = "stdmod") illustrates how std_selected() can be used to form nonparametric bootstrap percentile confidence interval for standardized regression coefficients ("betas") for regression models without a product term.

Further information on the functions can be found in their help pages (std_selected() and std_selected_boot()). For example, parallel computation can be used when doing bootstrapping, if the number of bootstrapping samples request is large.

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

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