PARTIAL_COEFS

Produces standardized regression coefficients, partial correlations, 
	and semi-partial correlations for a correlation matrix in which one variable 
 is a dependent or outcome variable and the other variables are independent
 or predictor variables.

Provides SPSS- and SAS-like output for least squares multiple
regression, logistic regression, and Poisson regression. 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. There are numerous options for model plots,
including plots of interactions for both lm and lme models.

Brian O'Connor

SIMPLE.REGRESSION

OLS, Moderated, Logistic, and Count Regressions Made Simple

PARTIAL_COEFS function

<dl> <dt>cormat</dt>
<dd><code></code>A correlation matrix. The DV (the dependent or outcome variable) must be in 
 the first row/column of cormat.
 Example: cormat = correls</dd> 	 
 <dt>modelRsq</dt>
<dd><code></code>(optional) The model Rsquared, which makes the computations slightly faster
 when it is available.
 Example: modelRsq = .22</dd> 	 	 
 <dt>verbose</dt>
<dd><code></code>Should detailed results be displayed in console? The options are: TRUE (default) or FALSE.</dd></dl>

Arguments

Author

Standardized coefficients and partial correlations for multiple regression — PARTIAL_COEFS

<dl>

 <dt>cormat</dt>
<dd><code></code>A correlation matrix. The DV (the dependent or outcome variable) must be in 
 the first row/column of cormat.
 Example: cormat = correls</dd>

 	 
 <dt>modelRsq</dt>
<dd><code></code>(optional) The model Rsquared, which makes the computations slightly faster
 when it is available.
 Example: modelRsq = .22</dd>

 	 	 
 <dt>verbose</dt>
<dd><code></code>Should detailed results be displayed in console? The options are: TRUE (default) or FALSE.</dd>

</dl>

PARTIAL_COEFS: Standardized coefficients and partial correlations for multiple regression

Description

Usage

Value

Arguments

Author

References

Examples