(Version 0.2.10.1, updated on 2024-03-26, release history)
(Important changes since 0.2.0.0: Bootstrap confidence intervals and variance-covariance matrix of estimates are the defaults of confint() and vcov() for the output of std_selected_boot().)
This package includes functions for computing a standardized moderation effect and forming its confidence interval by nonparametric bootstrapping correctly. It was described briefly in the following publication (OSF project page). It supports moderated regression conducted by stats::lm() and path analysis with product term conducted by lavaan::lavaan().
- 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.
More information on this package:
https://sfcheung.github.io/stdmod/
Quick Links:
stdmod: A quick start on how to use
std_selected()andstd_selected_boot(), the two main functions, to standardize selected variables in a regression model and refit the model.moderation: How to use
std_selected()andstd_selected_boot()to compute standardized moderation effect and form its nonparametric bootstrap confidence interval.std_selected: How to use
std_selected()to mean center or standardize selected variables in any regression models, and usestd_selected_boot()to form nonparametric bootstrap confidence intervals for standardized regression coefficients (betas in psychology literature).plotmod: How to generate a typical plot of moderation effect using
plotmod().cond_effect: How to compute conditional effects of the predictor for selected levels of the moderator, and form nonparametric bootstrap confidence intervals these effects.
Installation
The stable CRAN version can be installed by install.packages():
install.packages("stdmod")The latest version of this package at GitHub can be installed by remotes::install_github():
remotes::install_github("sfcheung/stdmod")Implementation
The main function, std_selected(), accepts an lm() output, standardizes variables by users, and update the results. If interaction terms are present, they will be formed after the standardization. If bootstrap confidence intervals are requested using std_selected_boot(), both standardization and regression will be repeated in each bootstrap sample, ensuring that the sampling variability of the standardizers (e.g., the standard deviations of the selected variables), are also taken into account.