Hi,
I am trying to run a linear mixed effect model on data. I have 17
longitudinal subjects and 36 single subjects, and this is the code I'm using
(below). So, INDEX1 is the column with brain volumns, and the predictors
are gort and age, by time ID (time they were seen).
I believe my data is set up the right way, but when I run it, I get DF for
Intercept is 49, and DF for slope is 13? Why?
lme.gort=lme(Volume ~ GORT_FLUENCY+AGE, random = ~ 1 | TIMEID, data =
subset(vol_data, INDEX1=='LH_FUSIFORM'), na.action=na.omit)
fit_vol_model1 <- function(df){
tryCatch(lme.gort <- lme(Volume ~ GORT_FLUENCY+AGE, random = ~ 1 | UID,
data=df, na.action=na.omit), error=function(err) tag <<-1)
data.frame(Term = rownames(anova(lme.gort)), anova(lme.gort))
+ }
models = list()
models$anova = ddply(vol_data, c("INDEX1"), fit_vol_model1)
summary(lme.gort)
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