(Version 0.2.9.1, updated on 2023-11-14, release history)
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
- 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:
std_selected: How to use
std_selected()to mean center or standardize selected variables in any regression models, and use
std_selected_boot()to form nonparametric bootstrap confidence intervals for standardized regression coefficients (betas in psychology literature).
cond_effect: How to compute conditional effects of the predictor for selected levels of the moderator, and form nonparametric bootstrap confidence intervals these effects.
The stable CRAN version can be installed by
The latest version of this package at GitHub can be installed by
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.