data("ships", package = "MASS")
Shipmodel <- vglm(incidents ~ type + year + period,
poissonff, offset = log(service),
data = ships, subset = (service > 0))
# Easiest form of input
fit1 = rcim(Qvar(Shipmodel, "type"), uninormal("explink"), maxit=99)
qvar(fit1) # Quasi-variances
qvar(fit1, se = TRUE) # Quasi-standard errors
# Manually compute them:
(quasiVar <- exp(diag(fitted(fit1))) / 2) # Version 1
(quasiVar <- diag(predict(fit1)[, c(TRUE, FALSE)]) / 2) # Version 2
(quasiSE <- sqrt(quasiVar))
if (FALSE) qvplot(fit1, col = "green", lwd = 3, scol = "blue",
slwd = 2, las = 1)
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