CrossValParams-class: Parameters for Cross-validation Specification

In DarioS/ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

Parameters for Cross-validation Specification

Description

Collects and checks necessary parameters required for cross-validation by runTests.

Usage

CrossValParams(
  samplesSplits = c("Permute k-Fold", "Permute Percentage Split", "Leave-k-Out",
    "k-Fold"),
  permutations = 100,
  percentTest = 25,
  folds = 5,
  leave = 2,
  tuneMode = c("Resubstitution", "Nested CV", "none"),
  adaptiveResamplingDelta = NULL,
  parallelParams = bpparam()
)

Arguments

samplesSplits	Default: "Permute k-Fold". A character value specifying what kind of sample splitting to do.
permutations	Default: 100. Number of times to permute the data set before it is split into training and test sets. Only relevant if samplesSplits is either "Permute k-Fold" or "Permute Percentage Split".
percentTest	The percentage of the data set to assign to the test set, with the remainder of the samples belonging to the training set. Only relevant if samplesSplits is "Permute Percentage Split".
folds	The number of approximately equal-sized folds to partition the samples into. Only relevant if samplesSplits is "Permute k-Fold" or "k-Fold".
leave	The number of samples to generate all possible combination of and use as the test set. Only relevant if samplesSplits is "Leave-k-Out". If set to 1, it is the traditional leave-one-out cross-validation, sometimes written as LOOCV.
tuneMode	Default: Resubstitution. The scheme to use for selecting any tuning parameters.
adaptiveResamplingDelta	Default: NULL. If not null, adaptive resampling of training samples is performed and this number is the difference in consecutive iterations that the class probability or risk of all samples must change less than for the iterative process to stop. 0.01 was used in the original publication.
parallelParams	An instance of BiocParallelParam specifying the kind of parallelisation to use. Default is to use two cores less than the total number of cores the computer has, if it has four or more cores, otherwise one core, as is the default of bpparam. To make results fully reproducible, please choose a specific back-end depending on your operating system and also set RNGseed to a number.

Author(s)

Dario Strbenac

Examples

  CrossValParams() # Default is 100 permutations and 5 folds of each.
  snow <- SnowParam(workers = 2, RNGseed = 999)
  CrossValParams("Leave-k-Out", leave = 2, parallelParams = snow)
  # Fully reproducible Leave-2-out cross-validation on 4 cores,
  # even if feature selection or classifier use random sampling.

DarioS/ClassifyR documentation built on Nov. 4, 2024, 1:06 p.m.