```
# NOT RUN {
# Fit the proportional odds model:
pneumo <- transform(pneumo, let = log(exposure.time))
(fit1 <- vglm(cbind(normal, mild, severe) ~ sm.bs(let, 3),
cumulative(parallel = TRUE, reverse = TRUE), data = pneumo))
coef(fit1, matrix = TRUE)
constraints(fit1) # Parallel assumption results in this
constraints(fit1, type = "term") # Same as the default ("vlm"-type)
is.parallel(fit1)
# An equivalent model to fit1 (needs the type "term" constraints):
clist.term <- constraints(fit1, type = "term") # "term"-type constraints
# cumulative() has no 'zero' argument to set to NULL (a good idea
# when using the 'constraints' argument):
(fit2 <- vglm(cbind(normal, mild, severe) ~ sm.bs(let, 3), data = pneumo,
cumulative(reverse = TRUE), constraints = clist.term))
abs(max(coef(fit1, matrix = TRUE) -
coef(fit2, matrix = TRUE))) # Should be zero
# Fit a rank-1 stereotype (RR-multinomial logit) model:
fit <- rrvglm(Country ~ Width + Height + HP, multinomial, data = car.all)
constraints(fit) # All except the first are the estimated A matrix
# }
```

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