nvalidate(eset, class="class", ngenes = c(5, 10, 20, 50, 100, 200, 500, 1000), method = "welch.test", dist="cor", ntrain ="balanced", nrep = 200, hparam = 0.75)nvalidation object, see nvalidation.object for details.
Objects of this class have a method for the function plot.
exprs(eset) contains the expression signatures of the patients in the columns. The character vector pData(eset)[[class]] contains the class membership of each sample or patient. Only tow-class problems are supported.The hyperparameter hparam describes the minimum number of
samples in each class after applying shift/throw. For copa, ort and os the hyperparameter specifies the quantile that has to be exceeded in order to consider a sample as an outlier. Typical values are 0.75 (default), 0.9, 0.95.
Validation is implemented in a multiple random validation protocol [1]. For each training set size, nrep training sets are randomly drawn from the patients. Features are selected and the centroid is calculated for each of the two classes in feature space. The test samples are classified to the class with the nearest centroid.
Four methods are available for calculation of the distance between test samples and the centroids: euclidean distance, euclidean distance after centering, angle and Pearson correlation. Calculation of distances is executed using the internal function get.d.
Feature selection, classification and validation are for predictors that include ngenes features.
nvalidation