Dear R users, I have a question of how to do some specific cell to cell comparisons on a R x C contingency table. The table is a 3 x 5 table with frequency / count data.
> langcons.table <- table(lang, cons) > langcons.table[cbind(lang,cons)] <- freq > langcons.table Adj Int Oth Pas Tra C 69 221 17 3 198 E 56 214 33 31 174 J 36 291 8 9 164 I know how to do an independent model test using Poisson in glm > glm.out1 <- glm(freq~lang+cons, family=poisson, data=langcons.data) > summary(glm.out1) And then fit the saturated model > glm.out2 <- glm(freq~lang*cons, family=poisson, data=langcons.data) > summary(glm.out2) However, the results are difficult to interpret: C and Adj are used to as a baseline. And I can only see main effects and interactions and *always according to the baseline*. Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) lang1 lang2 cons1 cons2 cons3 cons4 lang1:cons1 lang2:cons1 lang1:cons2 lang2:cons2 lang1:cons3 lang2:cons3 lang1:cons4 lang2:cons4 If anyone know, please suggest me some way to do specific cell to cell comparison on such a contingency table. Say, to compare pairs of cells: along a column: 3 vs 31, 9 vs 31, 3 vs 9 along a row: 36 vs 9 or even across column and row: 36 vs 31, and 36 vs 3 Thanks for your help in advance. Best, John ______________________________________________ R-help@r-project.org 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.