Validate samples in labels, features, and metadata

This function checks if labels are available for all samples in features. Additionally validates metadata, if available.

validate.data(siamcat, verbose = 1)

Arguments

siamcat	an object of class siamcat-class
verbose	integer, control output: 0 for no output at all, 1 for only information about progress and success, 2 for normal level of information and 3 for full debug information, defaults to 1

Value

an object of class siamcat-class

Details

This function validates the data by checking that labels are available for all samples in the feature matrix. Furthermore, the number of samples per class is checked to ensure a minimum number. If metadata is available, the overlap between labels and metadata is checked as well.

This function is run when a siamcat-class object is created.

Examples

data(siamcat_example)

# validate.data should be run before completing the pipeline
# since the complete pipeline had been run on siamcat_example, we
# construct a new siamcat object for the example
feat <- orig_feat(siamcat_example)
label <- label(siamcat_example)
siamcat <- siamcat(feat=feat, label=label, validate=FALSE)
#> Warning: ### Not validating the SIAMCAT object!!!
#> 	Please be advised that some functions may not work correctly!
siamcat <- validate.data(siamcat, verbose=2)
#> + starting validate.data
#> + Keeping labels of 141 sample(s).
#> + finished validate.data in 0.007 s