cv_standard: The standard cross-validator (CV)
In emeyers/NDTr: The Neural Decoding Toolbox in R

Description Usage Arguments Details Examples

This object runs a decoding analysis where a classifier is repeatedly trained and tested using cross-validation.

cv_standard(
  datasource,
  classifier,
  feature_preprocessors,
  result_metrics = NULL,
  num_resample_runs = 50,
  test_only_at_training_time = FALSE,
  run_parallel = TRUE
)

`datasource`	A datasource (DS) object that will generate the training and test data.
`classifier`	A classifier (CS) object that will learn parameters based on the training data and will generate predictions based on the test data.
`feature_preprocessors`	A list of feature preprocessor (FP) objects that learn preprocessing parameters from the training data and apply preprocessing of both the training and test data based on these parameters.
`result_metrics`	A list of result metric (RM) objects that are used to evaluate the classification performance. If this is set to null then the rm_main_results(), rm_confusion_matrix() results metrics will be used.
`num_resample_runs`	The number of times the cross-validation should be run (i.e., "resample runs"), where on each run, new training and test sets are generated. If pseudo-populations are used (e.g., with the ds_basic), then new pseudo-populations will be generated on each resample run as well.
`test_only_at_training_time`	A boolean indicated whether the classifier should only be trained and tested at the same time point (i.e., if TRUE a temporal cross-decoding analysis will NOT be run). Setting this to true can potentially speed up the analysis and save memory at the cost of not calculated the temporal cross decoding results.
`run_parallel`	A boolean to indicate whether the code should be run in parallel. It is useful to set this to FALSE for debugging purposes or if you are running a job that takes a lot of memory and runtime is not of much concern.

A cross-validator object takes a datasource (DS), a classifier (CL), feature preprocessors (FP) and result metric (RM) objects, and runs multiple cross-validation cycles where:

A datasource (DS) generates training and test data splits of the data
Feature preprocessors (FPs) do preprocessing of the data
A classifier (CL) is trained and predictions are generated on a test set
Result metrics (RMs) assess the accuracy of the predictions and compile the results.

data_file <- system.file("extdata/ZD_150bins_50sampled.Rda",
  package = "NDTr"
)

ds <- ds_basic(data_file, "stimulus_ID", 18)
fps <- list(fp_zscore())
cl <- cl_max_correlation()

cv <- cv_standard(ds, cl, fps)