add.distancemode_rep and mode_fold)imp_sample_from)partition_disc(): set default value of arg buffer to 0 instead of NULL, fixes #61partition_disc(): set default value of arg buffer to 0 instead of NULL, fixes #61partition_loo(): Sequence along observations instead of columns.
Before, the train set was only composed of ncol observation. (#60)sperrorest() run sequentially by default again rather than in parallel.err_fun() throws an error during performance calculation.
An exemplary case would be a binary classification in which only one level of the response exists in the test data (due to spatial partitioning).future_lapply from future.apply instead of futuretrain_fun and test_fun are now handled correctly and eventual sub-sampling is correctly reflected to the resulting 'resampling' objectNA and a message is printed to the console. sperrorest() will continue normally and uses the successful folds to calculate the repetition error. This helps to run CV with many repetitions using models which do not always converge like maxnet(), gamm() or svm().ecuador has been adjusted to avoid exact duplicates of partitions when using partition_kmeans().parsperrorest() into sperrorest().
sperrorest() now runs in parallel using all available cores.runfolds() and runreps() are now doing the heavy lifting in the background. All modes are now running on the same code base. Before, all parallel modes were running on different code implementations.apply: calls pbmclapply() on Unix and pbapply() on Windows.future: calls future_lapply() with various future options (multiprocess, multicore, etc.).foreach: foreach() with various future options (multiprocess, multicore, etc.). Default option to cluster. This is also the overall default mode for sperrorest().sequential: sequential execution using future backend.repetition argument of sperrorest(). Specifying a range like repetition = 1:10 will also stay valid.sperrorest::parallel-modes comparing the various parallel modes.sperrorest::custom-pred-and-model-functions explaining why and how custom defined model and predict functions are needed for some model setups.do_try argument has been removed.error.fold, error.rep and err.train arguments have been removed because they are all calculated by default now.add parsperrorest(): This function lets you execute sperrorest() in parallel. It includes two modes (par.mode = 1 and par.mode = 2) which use different parallelization approaches in the background. See ?parsperrorest() for more details.
add partition.factor.cv(): This resampling method enables partitioning based
on a given factor variable. This can be used, for example, to resample
agricultural data, that is grouped by fields, at the agricultural field level
in order to preserve spatial autocorrelation within fields.
sperrorest() and parsperrorest(): Add benchmark item to returned object giving information about execution time, used cores and other system details.
Changes to functions:
{sperrorest}(): Change argument naming. err.unpooled is now error.fold and err.pooled is now error.rep
sperrorest() and parsperrorest(): Change order and naming of returned object
sperrorestpoolederror is now sperrorestreperroradd package NEWS
add package vignette -> vignette("sperrorest-vignette", package = "sperrorest")
package is now ByteCompiled
Github repo of {sperrorest} now at https://github.com/giscience-fsu/sperrorest/