Centering several variables

One of the most common issues in regression analyses of even balanced experimental data is collinearity between main effects and interactions. To avoid this problem, a simple first step is to center all predictors. In my experience folks often fail to do that simply because it’s a bit more work and we’re all lazy. So here’s an attempt at a simple R function that takes single variables as well as entire dataframes.Synopsis: Outputs the centered values of the input variable, which can be a numeric variable, a factor, or a data frame.

• If the input is a numeric variable, the output is the centered variable.
• If the input is a factor, the output is a numeric variable with centered factor level values. That is, the factor’s levels are converted into numerical values in their inherent order (if not specified otherwise,  R defaults to alphanumerical order). More specifically, this centers any binary factor so that the value below 0 will be the 1st level of the original factor, and the value above 0 will be the 2nd level.
• If the input is a data frame or matrix, the output is a new matrix of the same dimension and with the centered values and column names that correspond to the colnames() of the input preceded by “c” (e.g. “Variable1” will be “cVariable1”).

I haven’t tested it extensively yet, but I used it for a couple of different problems and it generally works. Nothing big, but maybe useful (updated 7/30/10 to further simplify the function):

myCenter= function(x) {
	if (is.numeric(x)) { return(x - mean(x, na.rm=T)) }
	if (is.factor(x)) {
		x= as.numeric(x)
		return(x - mean(x, na.rm=T))
	}
	if (is.data.frame(x) || is.matrix(x)) {
		m= matrix(nrow=nrow(x), ncol=ncol(x))
		colnames(m)= paste("c", colnames(x), sep="")
		for (i in 1:ncol(x)) {
			m[,i]= myCenter(x[,i])
		}
		return(as.data.frame(m))
	}
}

Please let us know whether this works.