```
##anuran larvae example from Mazerolle (2006)
data(min.trap)
##assign "UPLAND" as the reference level as in Mazerolle (2006)
min.trap$Type <- relevel(min.trap$Type, ref = "UPLAND")
##set up candidate models
Cand.mod <- list()
##global model
Cand.mod[[1]] <- glm(Num_anura ~ Type + log.Perimeter + Num_ranatra,
family = poisson, offset = log(Effort),
data = min.trap)
Cand.mod[[2]] <- glm(Num_anura ~ Type + log.Perimeter, family = poisson,
offset = log(Effort), data = min.trap)
Cand.mod[[3]] <- glm(Num_anura ~ Type + Num_ranatra, family = poisson,
offset = log(Effort), data = min.trap)
Cand.mod[[4]] <- glm(Num_anura ~ Type, family = poisson,
offset = log(Effort), data = min.trap)
Cand.mod[[5]] <- glm(Num_anura ~ log.Perimeter + Num_ranatra,
family = poisson, offset = log(Effort),
data = min.trap)
Cand.mod[[6]] <- glm(Num_anura ~ log.Perimeter, family = poisson,
offset = log(Effort), data = min.trap)
Cand.mod[[7]] <- glm(Num_anura ~ Num_ranatra, family = poisson,
offset = log(Effort), data = min.trap)
Cand.mod[[8]] <- glm(Num_anura ~ 1, family = poisson,
offset = log(Effort), data = min.trap)
##check c-hat for global model
c_hat(Cand.mod[[1]]) #uses Pearson's chi-square/df
##note the very low overdispersion: in this case, the analysis could be
##conducted without correcting for c-hat as its value is reasonably close
##to 1
##assign names to each model
Modnames <- c("type + logperim + invertpred", "type + logperim",
"type + invertpred", "type", "logperim + invertpred",
"logperim", "invertpred", "intercept only")
##compute confidence set based on 'raw' method
confset(cand.set = Cand.mod, modnames = Modnames, second.ord = TRUE,
method = "raw")
##example with linear mixed model
if (FALSE) {
require(nlme)
##set up candidate model list for Orthodont data set shown in Pinheiro
##and Bates (2000: Mixed-effect models in S and S-PLUS. Springer Verlag:
##New York.)
Cand.models <- list()
Cand.models[[1]] <- lme(distance ~ age, random = ~age | Subject,
data = Orthodont, method = "ML")
Cand.models[[2]] <- lme(distance ~ age + Sex, data = Orthodont,
random = ~ 1 | Subject, method = "ML")
Cand.models[[3]] <- lme(distance ~ 1, data = Orthodont,
random = ~ 1 | Subject, method = "ML")
##create a vector of model names
Modnames <- paste("mod", 1:length(Cand.models), sep = "")
##compute confidence set based on 'raw' method
confset(cand.set = Cand.models, modnames = Modnames, second.ord = TRUE,
method = "raw")
##round to 4 digits after decimal point
print(confset(cand.set = Cand.models, modnames = Modnames,
second.ord = TRUE, method = "raw"), digits = 4)
confset(cand.set = Cand.models, modnames = Modnames, second.ord = TRUE,
level = 0.9, method = "raw")
##compute confidence set based on 'ordinal' method
confset(cand.set = Cand.models, modnames = Modnames, second.ord = TRUE,
method = "ordinal")
##compute confidence set based on 'ratio' method
confset(cand.set = Cand.models, modnames = Modnames, second.ord = TRUE,
method = "ratio", delta = 4)
confset(cand.set = Cand.models, modnames = Modnames, second.ord = TRUE,
method = "ratio", delta = 8)
detach(package:nlme)
}
```

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