lmres: Moderated regression with residual centering

In pequod: Moderated Regression Package

Description

Fit moderated linear regression with both residual centering and mean centering methods.

Usage

lmres(formula, data, residual_centering, centered, ...)
## Default S3 method:
lmres(formula, data, residual_centering=FALSE, centered = "none", ...)

Arguments

formula	an object of class "formula": a symbolic description of the model to be fitted.
data	a data frame
centered	variables wich must be centered
residual_centering	"FALSE" generate a moderated using standard lm regression, "TRUE" generate a moderated regression with residuals centering
...

Details

Moderated regression without residual centering : For any interaction term, the product is computed and entered in the final model. In order to perform a mean centered moderated regression, predictors must be centered Moderated regression with residual centering: For any interaction term with order n, a regression with low order terms (n-1) is computed, and Interaction residuals are entered in the final model.

Value

lmres returns an object of class "lmres".

An object of class "lmres" is a list containing at least the following components:

regr.order	the numeric order of the fitted linear model
formula.StepI	the formula of the first order regression
formula.StepII	(only where relevant) the formula of the second order regression
formula.Stepfin	the formula of the x (max(x)=3) order regression
beta.StepI	a named vector of standardized coefficients for the first order regression
beta.StepII	(only where relevant) a named vector of standardized coefficients for the second order regression
beta.Stepfin	a named vector of standardized coefficients for the x (max(x)=3) order regression
StepI	a lm object for the first order regression
StepII	(only where relevant) a lm object for the second order regression
Stepfin	a lm object for the x (max(x)=3) order regression
F_change	is a list containing F change statistics

Author(s)

Alberto Mirisola and Luciano Seta

References

Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the Merits of Orthogonalizing Powered and Product Terms: Implications for Modeling Interactions Among Latent Variables. Structural Equation Modeling, 13(4), 497-519.

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

See Also

“summary.lmres”

Examples

	
	## moderated regression with mean centering
	library(car)
	data(Ginzberg)
	model1<-lmres(adjdep~adjsimp*adjfatal, centered=c("adjsimp", "adjfatal"),
	data=Ginzberg)
	
	## moderated regression with mean centering
	library(car)
	data(Ginzberg)
	model1<-lmres(adjdep~adjsimp*adjfatal, centered=c("adjsimp", "adjfatal"),
	data=Ginzberg)
	## moderated regression with mean centering
	model2<-lmres(adjdep~adjsimp*adjfatal,residual_centering=TRUE,
	centered=c("adjsimp", "adjfatal"), data=Ginzberg)
	## three way interaction with mean centering
	library(car)
	data(Highway1)
	model3<-lmres(rate~len*trks*sigs1, centered=c("len","trks","sigs1"),data=Highway1)

## The function is currently defined as
function (formula, data, centered, ...) 
UseMethod("lmres")

Example output

Loading required package: ggplot2
Loading required package: car
Loading required package: carData
function (formula, data, centered, ...) 
UseMethod("lmres")

pequod documentation built on May 2, 2019, 7:11 a.m.