# Automated model selection using parallel computation Parallelized version of `dredge`. utf-8 ```r pdredge(global.model, cluster = NULL, 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, deps = attr(allTerms0, "deps"), check = FALSE, ...) ``` ## Arguments - `global.model, beta, rank, fixed, m.lim, m.max, m.min, subset, varying, extra, ct.args, deps, ...`: see `dredge`. - `evaluate`: whether to evaluate and rank the models. If `FALSE`, a list of unevaluated `call`s is returned and `cluster` is not used. - `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 `"cluster"` object, or `NULL` for a single threaded execution. - `check`: either integer or logical value controlling how much checking for existence and correctness of dependencies is done on the cluster nodes. See Details . ## Details All the dependencies for fitting the `global.model`, including the data and any objects that the modelling function will use must be exported to 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 will cause false errors if some arguments of the model call (other than `subset`) would be evaluated in the `data` environment. In that case is desirable to use `check = FALSE` (the default). If `check` is `TRUE` or greater than one, `pdredge` will compare the `global.model` updated on the cluster nodes with the one given as an argument. ## Note As of version 1.45.0, using `pdredge` directly is deprecated. Use `dredge` instead and provide `cluster` argument. ## Returns See `dredge`. ## Author(s) Kamil Barto ## See Also `makeCluster` and other cluster related functions in packages `parallel` or `snow`. ## Examples ```r # 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(dredge(fm1, cluster = FALSE, subset = msubset, varying = varying.link))) print(system.time(pdd <- dredge(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 <- dredge(ufm.mallard, # subset = `p(date)` | !`p(I(date^2))`, rank = AIC))) system.time(print(pdd2 <- dredge(ufm.mallard, cluster = 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) stopCluster(clust) ```

pdredge function