CrossValidation: Cross validation
In cma2015/RAP: Rank aggregation-based data fusion for gene prioritization

Description Usage Arguments Value Author(s) Examples

View source: R/CrossValidation.R

Performance evaluation of the integrative random forest-based gene prioritization algorithm RafSee in distinguishing positives and negatives.

1 2	CrossValidation(seed = 1, featureMat, positives, negatives, cross = 10, cpus = 1)

`seed`	An integer number specifying a random seed for randomly partitioning dataset.
`featureMat`	A numeric feature matrix.
`positives`	A character vector representing positive samples.
`negatives`	A character vector representing negative samples.
`cross`	Number of fold for cross validation.
`cpus`	An integer number specifying the number of cpus to be used for parallel computing.

A list containing results from each fold cross validation including:

`positives.train`	positive samples used to train prediction model.
`negatives.train`	negative samples used to train prediction model.
`positives.test`	positive samples used to test prediction model.
`negatives.test`	negative samples used to test prediction model.
`positives.train.score`	scores of positive samples in training dataset predicted by random foreste.
`negatives.train.score`	scores of negative samples in training dataset predicted by random forest.
`positives.test.score`	scores of positive samples in testing dataset predicted by random forest.
`negatives.test.score`	scores of negative samples in testing dataset predicted by random forest.
`train.AUC`	AUC value of random forest on training dataset.
`test.AUC`	AUC value of random forest on testing dataset.s

Jingjing Zhai, Chuang Ma

  ## Not run: 
    positives <- c("AT1G01060", "AT1G09530", "AT1G09570", "AT1G12610")
    cvRes <- CrossValidation(featureMat = featureMat, positives = positives, 
                      negatives  = negatives, cpus = 1)
    ## featureMat can be calculated by function FeatureExtract
    ## negatives can be calculated by function selectNegSamples
  
## End(Not run)