if(require(xtable)) {
##model selection
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
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-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)
}
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