Hi,
I have analyzed my data using log-linear model as seen below:
> yes.no <- c("Yes","No")
> tk <- c("On","Off")
> ats <- c("S","V","M")
> L <- gl(2,1,12,yes.no)
> T <- gl(2,2,12,tk)
> A <- gl(3,4,12,ats)
> n <- c(1056,4774,22,283,326,2916,27,360,274,1770,15,226)
> library(MASS)
> l.loglm <- data.frame(A,T,L,n)
> l.loglm
A T L n
1 S On Yes 1056
2 S On No 4774
3 S Off Yes 22
4 S Off No 283
5 V On Yes 326
6 V On No 2916
7 V Off Yes 27
8 V Off No 360
9 M On Yes 274
10 M On No 1770
11 M Off Yes 15
12 M Off No 226
Model comparison based on likelihood ratio chi-square statistics revealed that
the 3-way interaction (saturated) model was marginally significantly different
from the 2-way association model (see below):
> anova(loglm.null,loglm.LA.LT.AT)
LR tests for hierarchical log-linear models
Model 1:
n ~ T + A + L
Model 2:
n ~ L:T + L:A + A:T
Deviance df Delta(Dev) Delta(df) P(> Delta(Dev)
Model 1 305.997600 7
Model 2 4.620979 2 301.376622 5 0.00000
Saturated 0.000000 0 4.620979 2 0.09921
Now, I'd like to run a post-hoc test and see which one of the 3 levels of
the variable "A" is significantly different from each other (S vs. V
vs. M).
I'd greatly appreciate if anyone can let me know how to run the post-hoc
test.
Thank you in advance!
