```
# NOT RUN {
# Example 1
bdata <- data.frame(N = 10, mu = 0.5, rho = 0.8)
bdata <- transform(bdata,
y = rbetabinom(100, size = N, prob = mu, rho = rho))
fit <- vglm(cbind(y, N-y) ~ 1, betabinomial, data = bdata, trace = TRUE)
coef(fit, matrix = TRUE)
Coef(fit)
head(cbind(depvar(fit), weights(fit, type = "prior")))
# Example 2
fit <- vglm(cbind(R, N-R) ~ 1, betabinomial, lirat,
trace = TRUE, subset = N > 1)
coef(fit, matrix = TRUE)
Coef(fit)
t(fitted(fit))
t(depvar(fit))
t(weights(fit, type = "prior"))
# Example 3, which is more complicated
lirat <- transform(lirat, fgrp = factor(grp))
summary(lirat) # Only 5 litters in group 3
fit2 <- vglm(cbind(R, N-R) ~ fgrp + hb, betabinomial(zero = 2),
data = lirat, trace = TRUE, subset = N > 1)
coef(fit2, matrix = TRUE)
# }
# NOT RUN {
with(lirat, plot(hb[N > 1], fit2@misc$rho,
xlab = "Hemoglobin", ylab = "Estimated rho",
pch = as.character(grp[N > 1]), col = grp[N > 1]))
# }
# NOT RUN {
# cf. Figure 3 of Moore and Tsiatis (1991)
with(lirat, plot(hb, R / N, pch = as.character(grp), col = grp,
xlab = "Hemoglobin level", ylab = "Proportion Dead",
main = "Fitted values (lines)", las = 1))
smalldf <- with(lirat, lirat[N > 1, ])
for (gp in 1:4) {
xx <- with(smalldf, hb[grp == gp])
yy <- with(smalldf, fitted(fit2)[grp == gp])
ooo <- order(xx)
lines(xx[ooo], yy[ooo], col = gp)
}
# }
```

Run the code above in your browser using DataLab