validate(eset, class="class", ngenes = 50, method = "welch.test", dist="cor", ntrain = "balanced", nrep = 200, hparam = 0.75)pData(eset) that contains the class information. validation object, see validation.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 two-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, centered euclidean distance, angle and Pearson correlation. Calculation of distances is executed using the internal function get.d.
The parameter ntrain should be equal to one of the strings "balanced" or "prevalence" or a numeric matrix with two rows. For ntrain = "balanced", a balanced layout is used, i.e. half of the training set is chosen from each of the two classes. For ntrain = "prevalence" the training sets are balanced according to the prevalence of the two classes in the entire data set. Further, the user can manually specify the sizes of the training sets.
validation