```
##Mazerolle (2006) frog water loss example
data(dry.frog)
##setup a subset of models of Table 1
Cand.models <- list( )
Cand.models[[1]] <- lm(log_Mass_lost ~ Shade + Substrate +
cent_Initial_mass + Initial_mass2,
data = dry.frog)
Cand.models[[2]] <- lm(log_Mass_lost ~ Shade + Substrate +
cent_Initial_mass + Initial_mass2 +
Shade:Substrate, data = dry.frog)
Cand.models[[3]] <- lm(log_Mass_lost ~ cent_Initial_mass +
Initial_mass2, data = dry.frog)
Cand.models[[4]] <- lm(log_Mass_lost ~ Shade + cent_Initial_mass +
Initial_mass2, data = dry.frog)
Cand.models[[5]] <- lm(log_Mass_lost ~ Substrate + cent_Initial_mass +
Initial_mass2, data = dry.frog)
##create a vector of names to trace back models in set
Modnames <- paste("mod", 1:length(Cand.models), sep = " ")
##generate AICc table
aictab(cand.set = Cand.models, modnames = Modnames, sort = TRUE)
##round to 4 digits after decimal point and give log-likelihood
print(aictab(cand.set = Cand.models, modnames = Modnames, sort = TRUE),
digits = 4, LL = TRUE)
if (FALSE) {
##Burnham and Anderson (2002) flour beetle data
data(beetle)
##models as suggested by Burnham and Anderson p. 198
Cand.set <- list( )
Cand.set[[1]] <- glm(Mortality_rate ~ Dose, family =
binomial(link = "logit"), weights = Number_tested,
data = beetle)
Cand.set[[2]] <- glm(Mortality_rate ~ Dose, family =
binomial(link = "probit"), weights = Number_tested,
data = beetle)
Cand.set[[3]] <- glm(Mortality_rate ~ Dose, family =
binomial(link ="cloglog"), weights = Number_tested,
data = beetle)
##check c-hat
c_hat(Cand.set[[1]])
c_hat(Cand.set[[2]])
c_hat(Cand.set[[3]])
##lowest value of c-hat < 1 for these non-nested models, thus use
##c.hat = 1
##set up named list
names(Cand.set) <- c("logit", "probit", "cloglog")
##compare models
##model names will be taken from the list if modnames is not specified
res.table <- aictab(cand.set = Cand.set, second.ord = FALSE)
##note that delta AIC and Akaike weights are identical to Table 4.7
print(res.table, digits = 2, LL = TRUE) #print table with 2 digits and
##print log-likelihood in table
print(res.table, digits = 4, LL = FALSE) #print table with 4 digits and
##do not print log-likelihood
}
##two-way ANOVA with interaction
data(iron)
##full model
m1 <- lm(Iron ~ Pot + Food + Pot:Food, data = iron)
##additive model
m2 <- lm(Iron ~ Pot + Food, data = iron)
##null model
m3 <- lm(Iron ~ 1, data = iron)
##candidate models
Cand.aov <- list(m1, m2, m3)
Cand.names <- c("full", "additive", "null")
aictab(Cand.aov, Cand.names)
##single-season occupancy model example modified from ?occu
if (FALSE) {
require(unmarked)
##single season example modified from ?occu
data(frogs)
pferUMF <- unmarkedFrameOccu(pfer.bin)
##add fake covariates
siteCovs(pferUMF) <- data.frame(sitevar1 = rnorm(numSites(pferUMF)),
sitevar2 = runif(numSites(pferUMF)))
##observation covariates
obsCovs(pferUMF) <- data.frame(obsvar1 = rnorm(numSites(pferUMF) *
obsNum(pferUMF)))
##set up candidate model set
fm1 <- occu(~ obsvar1 ~ sitevar1, pferUMF)
fm2 <- occu(~ 1 ~ sitevar1, pferUMF)
fm3 <- occu(~ obsvar1 ~ sitevar2, pferUMF)
fm4 <- occu(~ 1 ~ sitevar2, pferUMF)
##assemble models in named list (alternative to using 'modnames' argument)
Cand.mods <- list("fm1" = fm1, "fm2" = fm2, "fm3" = fm3, "fm4" = fm4)
##compute table
aictab(cand.set = Cand.mods, second.ord = TRUE)
detach(package:unmarked)
}
```

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