knitr::opts_chunk$set(echo = TRUE)
Parallelization was already available using parallel
package (through mclApply
function)
but was not very easy to configure.
Now SCGLR is using the more modern future
package and
it's companion package future.apply
for apply-like functions (both are needed but
the later will be handled internally).
See https://github.com/HenrikBengtsson/future for more information.
It will be easier to switch from simple parallelization to more complex architecture.
Parallelization is available for scglrCrossVal
and scglrThemeBackward
functions
(more may be added later).
library(future) library(future.apply) # line not needed but package must be installed # launch a local cluster backed by R sessions plan(multisession) ...
scglrCrossVal
exampleNothing changes except the insertion of the code above.
library(SCGLR) library(future) library(future.apply) # line not needed but package must be installed # launch a local cluster backed by R sessions plan(multisession) # load sample data data(genus) # get variable names from dataset n <- names(genus) ny <- n[grep("^gen",n)] # Y <- names that begins with "gen" nx <- n[-grep("^gen",n)] # X <- remaining names # remove "geology" and "surface" from nx # as surface is offset and we want to use geology as additional covariate nx <-nx[!nx%in%c("geology","surface")] # build multivariate formula # we also add "lat*lon" as computed covariate form <- multivariateFormula(ny,c(nx,"I(lat*lon)"),A=c("geology")) # define family fam <- rep("poisson",length(ny)) # cross validation genus.cv <- scglrCrossVal(formula=form, data=genus, family=fam, K=5, offset=genus$surface)
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