Moderated regression with residual centering
Fit moderated linear regression with both residual centering and mean centering methods.
lmres(formula, data, residual_centering, centered, ...) ## Default S3 method: lmres(formula, data, residual_centering=FALSE, centered = "none", ...)
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 |
... |
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.
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 |
Alberto Mirisola and Luciano Seta
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.
“summary.lmres”
## 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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.