```
if(require(xtable)) {
##model selection example
data(dry.frog)
##setup candidate models
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)
Model.names <- c("additive", "interaction", "no shade")
##model selection table - AICc
out <- aictab(cand.set = Cand.models, modnames = Model.names)
xtable(out)
##exclude AICc and LL
xtable(out, include.AICc = FALSE, include.LL = FALSE)
##remove row names and add caption
print(xtable(out, caption = "Model selection based on AICc"),
include.rownames = FALSE, caption.placement = "top")
##model selection table - BIC
out2 <- bictab(cand.set = Cand.models, modnames = Model.names)
xtable(out2)
##exclude AICc and LL
xtable(out2, include.BIC = FALSE, include.LL = FALSE)
##remove row names and add caption
print(xtable(out2, caption = "Model selection based on BIC"),
include.rownames = FALSE, caption.placement = "top")
##model-averaged estimate of Initial_mass2
mavg.mass <- modavg(cand.set = Cand.models, parm = "Initial_mass2",
modnames = Model.names)
#model-averaged estimate
xtable(mavg.mass, print.table = FALSE)
#table with contribution of each model
xtable(mavg.mass, print.table = TRUE)
##model-averaged predictions for first 10 observations
preds <- modavgPred(cand.set = Cand.models, modnames = Model.names,
newdata = dry.frog[1:10, ])
xtable(preds)
}
##example of diagnostics
if (FALSE) {
if(require(unmarked)){
##distance sampling example from ?distsamp
data(linetran)
ltUMF <- with(linetran, {
unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4),
siteCovs = data.frame(Length, area, habitat),
dist.breaks = c(0, 5, 10, 15, 20),
tlength = linetran$Length * 1000, survey = "line",
unitsIn = "m")
})
##summarize counts across distance classes
xtable(countDist(ltUMF), table.countDist = "distance")
##summarize counts across all sites
xtable(countDist(ltUMF), table.countDist = "count")
##Half-normal detection function
fm1 <- distsamp(~ 1 ~ 1, ltUMF)
##determine parameters with highest SE's
xtable(checkParms(fm1))
}
}
```

Run the code above in your browser using DataLab