On Jul 21, 2010, at 8:07 AM, Tsunhin John Wong wrote:
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.
Even if you are daunted by the task of plugging the covariates into
the formula, exp(intercept+sum(beta_N*var_n)), you can always use the
predict function to create an estimate for all (or a specific set) of
the covariates. They come out on the log(rate) scale so would need to
be exponentiated. Consult your instructor for further help.
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
David Winsemius, MD
West Hartford, CT
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