set.seed(1)
data(yaish)
## Fit the "UNIDIFF" mobility model across education levels, leaving out
## the uninformative subtable for dest == 7:
##
unidiff <- gnm(Freq ~ educ*orig + educ*dest +
Mult(Exp(-1 + educ), -1 + orig:dest), family = poisson,
data = yaish, subset = (dest != 7))
## Deviance should be 200.3, 116 d.f.
##
## Look at the multipliers of the orig:dest association:
coefs.of.interest <- grep("Mult1.*educ", names(coef(unidiff)))
coef(unidiff)[coefs.of.interest]
##
## Mult1.Factor1.educ1 Mult1.Factor1.educ2 Mult1.Factor1.educ3 Mult1.Factor1.educ4
## -0.8150195 -1.0403857 -1.5584007 -1.8538570
## Mult1.Factor1.educ5
## -3.0643114
##
## Get standard errors for the contrasts with educ1:
##
getContrasts(unidiff, coefs.of.interest)
##
## [[1]]
## Estimate Std. Error quasiSE quasiVar
## Mult1.Factor1.educ1 0.0000000 0.0000000 0.0975751 0.00952090
## Mult1.Factor1.educ2 -0.2253661 0.1611886 0.1288595 0.01660478
## Mult1.Factor1.educ3 -0.7433812 0.2335042 0.2118164 0.04486617
## Mult1.Factor1.educ4 -1.0388374 0.3434049 0.3260719 0.10632288
## Mult1.Factor1.educ5 -2.2492918 0.9452228 0.9354512 0.87506893
##
## Table of model residuals:
##
residuals(unidiff)
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