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# Goodman, L. A. (1979) J. Am. Stat. Assoc., 74 (367), 537–552.
RChomog <- gnm(Freq ~ origin + destination + Diag(origin, destination) +
MultHomog(origin, destination), family = poisson,
data = occupationalStatus, verbose = FALSE)
test_that("RChomog model as expected for occupationalStatus data", {
# Model (8) Table 7A
pearson_chi_sq <- sum(na.omit(c(residuals(RChomog, type = "pearson")))^2)
expect_equal(round(deviance(RChomog), 2), 32.56)
expect_equal(round(pearson_chi_sq, 2), 31.21)
expect_equal(df.residual(RChomog), 34)
})
# Chan, T.W. and Goldthorpe, J.H. (2004)
# European Sociological Review, 20, 383–401.
### Fit an association model with homogeneous row-column effects
### Set diagonal elements to NA (rather than fitting exactly)
dat <- as.data.frame(friend)
id <- with(dat, r == c)
dat[id,] <- NA
rc2 <- gnm(Freq ~ r + c + instances(MultHomog(r, c), 2),
family = poisson, data = dat, iterStart = 0, verbose = FALSE)
test_that("RChomog2 model as expected for friend data", {
# association models not reported in original paper
pearson_chi_sq <- sum(na.omit(c(residuals(rc2, type = "pearson")))^2)
expect_equal(round(deviance(rc2), 2), 1006.91)
expect_equal(round(pearson_chi_sq, 2), 967.21)
expect_equal(df.residual(rc2), 810)
})
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