OLS, Moderated, Logistic, and Count Regressions Made Simple
Count data regression
Logistic regression
Moderated multiple regression
Ordinary least squares regression
Partial and semipartial correlations
Plots predicted values for a regression model
Plots of Johnson-Neyman regions of significance for interactions
Cohen's Set Correlation Analysis
SIMPLE.REGRESSION
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 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. There is also a function for partial and semipartial correlations and a function for conducting Cohen's set correlation analyses.