I am trying to reproduce the SPSS output for significance a linear trend among means when equal variances are not assumed. I have gratefully used code from http://www-personal.umich.edu/~gonzo/coursenotes/file3.pdf to create a function for calculating separate variances, which based on my searching I understand as the “equal variances not assumed” output in SPSS.
My problem/goal: I am only assessing polynomial orthogonal trends (mostly linear). I want to adapt the code creating the function so that the contrast argument can take pre-made contrast matrices rather than manually specifying the coefficients each time (room for typos!).
… I have tried those exact commands but receive Error in contrast %*% means : non-conformable arguments
. I have played around with the code but I can’t get it to work.
Code for creating the function from the notes:
sepvarcontrast <- function(dv, group, contrast) {
means <- c(by(dv, group, mean))
vars <- c(by(dv, group, var))
ns <- c(by(dv, group, length))
ihat <- contrast %*% means
t.denominator <- sqrt(contrast^2 %*% (vars/ns))
t.welch <- ihat/ t.denominator
num.contrast <- ifelse(is.null(dim(contrast)),1,dim(contrast)[1])
df.welch <- rep(0, num.contrast)
if (is.null(dim(contrast))) contrast <- t(as.matrix(contrast))
for (i in 1:num.contrast) {
num <- (contrast[i,]^2 %*% (vars))^2
den <- sum((contrast[i,]^2 * vars)^2 / (ns-1))
df.welch[i] <- num/den
}
p.welch <- 2*(1- pt(abs(t.welch), df.welch))
result <- list(ihat = ihat, se.ihat = t.denominator, t.welch = t.welch,
df.welch = df.welch, p.welch = p.welch)
return(result)
}
I would like to be able to use the function like this:
# Create a polynomial contrast matrix for 5 groups, then save
contr.mat5 <- contr.poly(5)
# Calculate separate variance
sepvarcontrast(dv, group, contrast = contr.mat5)
I have tried those exact commands to see if they would work but receive Error in contrast %*% means : non-conformable arguments
.
All suggestions are appreciated! I am still learning how to create a reprex...