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 5/12/09, based on suggestions by Matthew Carlson, Psychology, U. of Chicago):

myCenter <- function(x) {
	if (is.numeric(x)) { return(x - mean(x)) }
	if (is.factor(x)) {
		x <- as.numeric(x)
		return(x - mean(x))
	}
	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)) {
			if (is.factor(x[,i])) {
				y <- as.numeric(x[,i])
				m[,i] <- y - mean(y, na.rm=T)
                        }
			if (is.numeric(x[,i])) {
				m[,i] <- x[,i] - mean(x[,i], na.rm=T)
                        }
 		}
                return(as.data.frame(m))

        }
}

Please let us know whether this works.