Automated model selection using parallel computation
Parallelized version of dredge.
pdredge(global.model, cluster = NA,
beta = c("none", "sd", "partial.sd"), evaluate = TRUE, rank = "AICc",
fixed = NULL, m.lim = NULL, m.min, m.max, subset, trace = FALSE,
varying, extra, ct.args = NULL, check = FALSE, ...)global.model, beta, evaluate, rank |
see |
fixed, m.lim, m.max, m.min, subset, varying, extra, ct.args, ... |
see |
trace |
displays the generated calls, but may not work as expected since the models are evaluated in batches rather than one by one. |
cluster |
either a valid |
check |
either integer or logical value controlling how much checking for existence and correctness of dependencies is done on the cluster nodes. See ‘Details’. |
All the dependencies for fitting the global.model, including the data
and any objects the modelling function will use must be exported
into the cluster worker nodes (e.g. via clusterExport).
The required packages must be also loaded thereinto (e.g. via
clusterEvalQ(..., library(package)), before the cluster is used by
pdredge.
If check is TRUE or positive, pdredge tries to check whether
all the variables and functions used in the call to global.model are
present in the cluster nodes' .GlobalEnv before proceeding further.
This causes false errors if some arguments of the model call (other than
subset) would be evaluated in data environment. In that case
using check = FALSE (the default) is desirable.
If check is TRUE or greater than one, pdredge will
compare the global.model updated at the cluster nodes with the one
given as argument.
See dredge.
Kamil Bartoń
makeCluster and other cluster related functions in packages
parallel or snow.
# One of these packages is required:
## Not run: require(parallel) || require(snow)
# 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))
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)")
## End(Not run)
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