```
# NOT RUN {
# }
# NOT RUN {
# One of these packages is required:
# }
# NOT RUN {
require(parallel) || require(snow)
# }
# NOT RUN {
# From example(Beetle)
Beetle100 <- Beetle[sample(nrow(Beetle), 100, replace = TRUE),]
fm1 <- glm(Prop ~ dose + I(dose^2) + log(dose) + I(log(dose)^2),
data = Beetle100, family = binomial, na.action = na.fail)
msubset <- expression(xor(dose, `log(dose)`) & (dose | !`I(dose^2)`)
& (`log(dose)` | !`I(log(dose)^2)`))
varying.link <- list(family = alist(logit = binomial("logit"),
probit = binomial("probit"), cloglog = binomial("cloglog") ))
# Set up the cluster
clusterType <- if(length(find.package("snow", quiet = TRUE))) "SOCK" else "PSOCK"
clust <- try(makeCluster(getOption("cl.cores", 2), type = clusterType))
# }
# NOT RUN {
clusterExport(clust, "Beetle100")
# noticeable gain only when data has about 3000 rows (Windows 2-core machine)
print(system.time(dredge(fm1, subset = msubset, varying = varying.link)))
print(system.time(pdredge(fm1, cluster = FALSE, subset = msubset,
varying = varying.link)))
print(system.time(pdd <- pdredge(fm1, cluster = clust, subset = msubset,
varying = varying.link)))
print(pdd)
# }
# NOT RUN {
# Time consuming example with 'unmarked' model, based on example(pcount).
# Having enough patience you can run this with 'demo(pdredge.pcount)'.
library(unmarked)
data(mallard)
mallardUMF <- unmarkedFramePCount(mallard.y, siteCovs = mallard.site,
obsCovs = mallard.obs)
(ufm.mallard <- pcount(~ ivel + date + I(date^2) ~ length + elev + forest,
mallardUMF, K = 30))
clusterEvalQ(clust, library(unmarked))
clusterExport(clust, "mallardUMF")
# 'stats4' is needed for AIC to work with unmarkedFit objects but is not
# loaded automatically with 'unmarked'.
require(stats4)
invisible(clusterCall(clust, "library", "stats4", character.only = TRUE))
#system.time(print(pdd1 <- pdredge(ufm.mallard,
# subset = `p(date)` | !`p(I(date^2))`, rank = AIC)))
system.time(print(pdd2 <- pdredge(ufm.mallard, clust,
subset = `p(date)` | !`p(I(date^2))`, rank = AIC, extra = "adjR^2")))
# best models and null model
subset(pdd2, delta < 2 | df == min(df))
# Compare with the model selection table from unmarked
# the statistics should be identical:
models <- get.models(pdd2, delta < 2 | df == min(df), cluster = clust)
modSel(fitList(fits = structure(models, names = model.names(models,
labels = getAllTerms(ufm.mallard)))), nullmod = "(Null)")
# }
# NOT RUN {
stopCluster(clust)
# }
# NOT RUN {
# }
```

Run the code above in your browser using DataLab