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).
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Concepts, applications, and implementation. Guilford Press.
Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models
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process analysis: A regression-based approach (2nd ed.). Guilford Press.
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methods for experiments, quasi-experiments, and single-case studies. John Wiley & Sons.
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dependent variables. Oxford University Press.
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and prediction. (3rd ed.). Wadsworth Thomson Learning.