SIMPLE.REGRESSION-package: SIMPLE.REGRESSION

In SIMPLE.REGRESSION: OLS, Moderated, Logistic, and Count Regressions Made Simple

SIMPLE.REGRESSION

Description

Provides SPSS- and SAS-like output for least squares multiple regression, logistic regression, and count variable regressions. Detailed output is also provided for OLS moderated regression, interaction plots, and Johnson-Neyman regions of significance. The output includes standardized coefficients, partial and semi-partial correlations, collinearity diagnostics, plots of residuals, and detailed information about simple slopes for interactions. The output for some functions includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo (MCMC) analyses. There are numerous options for model plots.

The REGIONS_OF_SIGNIFICANCE function also provides Johnson-Neyman regions of significance and plots of interactions for both lm and lme models (lme models are from the nlme package). There is also a function for partial and semipartial correlations and a function for conducting Cohen's set correlation analyses.

References

Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373-400.

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates.

Darlington, R. B., & Hayes, A. F. (2017). Regression analysis and linear models: Concepts, applications, and implementation. Guilford Press.

Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. Springer.

Hayes, A. F. (2018a). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.

Huitema, B. (2011). The analysis of covariance and alternatives: Statistical methods for experiments, quasi-experiments, and single-case studies. John Wiley & Sons.

Johnson, P. O., & Fey, L. C. (1950). The Johnson-Neyman technique, its theory, and application. Psychometrika, 15, 349-367.

Lorah, J. A. & Wong, Y. J. (2018). Contemporary applications of moderation analysis in counseling psychology. Counseling Psychology, 65(5), 629-640.

Orme, J. G., & Combs-Orme, T. (2009). Multiple regression with discrete dependent variables. Oxford University Press.

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. (3rd ed.). Wadsworth Thomson Learning.

SIMPLE.REGRESSION documentation built on June 20, 2025, 9:07 a.m.