Hi all,
I am trying to fit an ANOVA model in R using the aov/lm commands. I have a
set of observational (i.e. no fixed experimental effects) data, in which I
have identified high and low clusters of the response variable. The design
is unbalanced, with 773 high cluster observations, and 523 low cluster
observations. I would like to test a set of 7 correlates to see if there are
significant differences in their means between the clusters: That is I have
one fixed effect with 2 levels, and a bunch of 7 continuous predictors. I
believe the correct model specification is an ANCOVA design(?)
I can fit this model in MINITAB using, say:
glm response = cluster;
covariate predictor1 predictor2 ... predictor7.
In R, if I specify the model using
cluster<-ordered(clusterlevels=c("Low","High"))
Model<-lm(predictor~response1+response2+ ... response7+cluster)
I can replicate the results from MINITAB, getting identical P and t values
when I do summary(lm(Model)), but the F values are all different (huge) when
I do summary(aov(Model)) for all correlates. The F value for the fixed
effect is correct. The P values for summary(aov(Model)) are all highly
significant too.
I would like to fit the model in R, both for consistency with my other
analysis, and because I use R on my home machine, and have to venture into
the university labs to use MINITAB.
Many thanks
Luke Spadavecchia
[[alternative HTML version deleted]]
