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From a previous topic(Maximum number of independent variables that can be entered into a multiple regression equation ) I am trying to extend the function into lmer for basic clinical psychology/neuropsychology studies with 2 factors (fact = time 1 and 2; fact2 = treatment 1 and 2), 1 continuous predictor and id (repeated 2 times as time is two levels) to compute minimum number of subjects to be included to explain each variable.

1 does this sound correct? 2 any improvements that might be useful?

fitlmer <- function(n, k) {
  # n: sample size
  # k: number of predictors
  # return linear model fit for given sample size and k predictors
  require(lme4)
  require(lmerTest)
  x <- data.frame(matrix(rnorm(n*k), nrow=n))
  names(x) <- paste("x", seq(k), sep="")
  x$y <- rnorm(n)
  x$fact <- rep(1:2)
  x$fact2 <- rep(1:2, each= 2)
  x$id <- rep(1:(n/2), each= 2)
  as.factor(x$fact)
  as.factor(x$fact2)
  summary(lmer(y~x1*fact*fact2+ (1|id), data=x), ddf = "Kenward-Roger")
}
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