I have a problem moving from multistratum aov analysis to lmer.
My dataset has observations of ampl at 4 levels of gapf and 2 levels of bl
on 6 subjects levels VP, with 2 replicates wg each, and is balanced.
Here is the summary of this set with aov:
Yesterday I was biten by a feature, which I find too dangerous.
I wanted to use a factor `Subject´ as index into a data frame, whose row
names were the levels of this factor. So there a 2 different possible
interpretations of this: Either Subject is coerced to numeric or to
character. The
I have been asked how to handle the following situation in R:
Given an unbalanced design of 3 crossed random effects, such as
subject, rater and item, how to estimate the variance components?
I know how to do it using lme, but this seems to be limited to
the nested case; or to use aov with error
I am trying to analyse a data with gls/lm using the following set of models
prcn.0.lm - lm( log10(Y)~(cond-1)+(cond-1):t ,prcn)
prcn.1.gls - gls( log10(Y)~(cond-1)+(cond-1):t ,prcn,cor=corAR1())
prcn.0.gls - gls( log10(Y)~(cond-1)+(cond-1):t ,prcn)
prcn.1m.gls - gls( log10(Y)~(cond-1)+(cond-1):t
