rockchalk (version 1.4)

meanCenter: meanCenter

Description

meanCenter selectively centers or standarizes variables in a regression model.

Usage

meanCenter(model, centerOnlyInteractors = TRUE,
    centerDV = FALSE, standardize = FALSE,
    centerContrasts = F)
  ## S3 method for class 'default':
meanCenter(model,
    centerOnlyInteractors = TRUE, centerDV = FALSE,
    standardize = FALSE, centerContrasts = F)

Arguments

model

a fitted regression model (presumably from lm)

centerOnlyInteractors

If false, all predictors in the regression data frame are centered before the regression is conducted.

centerDV

Should the dependent variable be centered?

standardize

Instead of simply mean-centering the variables, should they also be "standardized" by first mean-centering and then dividing by the estimated standard deviation.

centerContrasts

This function was originally intended only to center numeric variables. However, this option will ask centering of the numeric contrasts that are created in the fitting process.

Value

• A regression model of the same type as the input model, with attributes representing the names of the centered variables.

Details

The defaults will cause a regression's numeric interactive variables to be mean centered. If one wants all predictors to be centered, the option centerOnlyInteractors should be set to FALSE. The dependent variable will not be centered, unless the user explicitly requests it by setting centerDV = TRUE.

See Also

lmres

Examples

library(rockchalk)
N <- 100
dat <- genCorrelatedData(N=N, means=c(100,200), sds=c(20,30), rho=0.4, stde=10)
dat$x3 <- rnorm(100, m=40, s=4)

m1 <- lm(y ~ x1 * x2 + x3, data=dat)
summary(m1)
mcDiagnose(m1)

m1c <- meanCenter(m1)
summary(m1c)
mcDiagnose(m1c)

m2 <- lm(y ~ x1 * x2 + x3, data=dat)
summary(m2)
mcDiagnose(m2)

m2c <- meanCenter(m2, standardize = TRUE)
summary(m2c)
mcDiagnose(m2c)

m2c2 <- meanCenter(m2, centerOnlyInteractors = FALSE)
summary(m2c2)

m2c3 <- meanCenter(m2, centerOnlyInteractors = FALSE, centerDV = TRUE)
summary(m2c3)

dat <- genCorrelatedData(N=N, means=c(100,200), sds=c(20,30), rho=0.4, stde=10)
dat$x3 <- rnorm(100, m=40, s=4)
dat$x3 <- gl(4, 25, labels=c("none","some","much","total"))

m3 <- lm(y ~ x1 * x2 + x3, data=dat)
summary(m3)

m3c1 <- meanCenter(m3)
summary(m3c1)

m3c2 <- meanCenter(m3, centerContrasts=TRUE)
summary(m3c2)

## Not exactly the same as a "standardized" regression because the
## interactive variables are centered in the model frame,
## and the term "x1:x2" is never centered again.
m3c3 <- meanCenter(m3, centerDV=TRUE, centerContrasts=TRUE, centerOnlyInteractors=FALSE, standardize=TRUE)
summary(m3c3)

m3st <- standardize(m3)
summary(m3st)