As an addendum to my earlier post, I am having another difficulty with getting manova() to behave as I would like: when I specify contrasts for my independent variable(s), I am unsure of how to test them. This is a contrived example of both of my questions:
Assume three alertness measurements, "alert1" "alert2" "alert3", a within-subjects variable measuring their alertness at three timepoints, minutes after taking the drug (alert1), one hour (alert2), and two hours (alert3). The between-subjects variable is dosage, with dose==0 when subjects have had no drug, dose==1 when they have had a single dose, dose==2 when they have had a double dose. My intuition says to do the following: alert <- cbind(alert1,alert2,alert3) %*% contr.poly(3) contrasts(dose) <- matrix(c(2,-1,-1,0,1,-1),3,2) m <- manova(alert ~ dose) ...what I want is two main effects (dose and alert) and one interaction (dose by alert), but also "main effect" and "interaction" for the two individual contrasts for dose. For the main effect for alert, and all of the dose*alert interactions, I need the discriminant function loadings of my two alertness contrasts in order to interpret the manner in which alertness is varying (e.g., is it varying in a linear or quadratic way). m2 <- manova (alert ~ dose) summary(m2) ...gives me a test for the dose * alertness interaction. Good! But I can't seem to find the contrasts I asked for for dose. In univariate ANOVA, I usually just call summary.lm() which gives me t-test coefficients for each level of the dose contrast...but calling summary.lm on a manova object returns t-tests on three unnamed coefficients, with 27 error degrees of freedom (when it should be 26, as I am intending to compute both dosage contrasts simultaneously). Also, I cannot tell whether it is the linear or quadratic contrast that is contributing to the differentiation of dosage levels--this is why I need the discriminant function loadings. m2 <- lm( apply(cbind(alert1,alert2,alert3),1,mean) ~ dose) summary(m2) ...gives me a test for the two contrasts, which can be pooled to get a main effect. Excellent! ...finally, for the main effect of alertness, I'm more or less at a loss. The question is whether the three alertness conditions differ from each other, or whether some linear combination of the linear and quadratic contrast columns is significantly different from zero...and then the relative weightings of the linear and quadratic contrasts. Any suggestions? Thanks, Adam On Mon, 5 Feb 2007, Adam D. I. Kramer wrote: > Hello, > > I've been playing with the manova() function to do some pretty > straightforward multivariate analyses, and I can't for the life of me figure > out how to get at the discriminant functions used. When predicting several > variables simultaneously, it's important to be able to gauge how much each > variable is contributing to the analysis...a simple p-value isn't really > enough. I find examination of the discriminant function loadings to be a > good indicator of this. > > Thanks, > Adam Kramer > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
