predict_svmc: prediction based on hidden genome random forest classifier
In c7rishi/hidgenclassifier: Functions for Bayesian hierarchical hidden genome classifier

predict_svmc

R Documentation

prediction based on hidden genome random forest classifier

prediction based on hidden genome random forest classifier

predict_svmc(fit, Xnew, Ynew = NULL, ...)

predict_svm(fit, Xnew, Ynew = NULL, ...)

`fit`	fitted hidden genome SVM classifier (output of `fit_svmc()`)
`Xnew`	test data design (or meta-design) matrix (observations across rows and variables predictors/features across columns) for which predictions are to be made from a fitted model. For a typical hidden genome classifier this will be a matrix whose rows correspond to the test set tumors, and columns correspond to (normalized by some functions of the total mutation burdens in tumors) binary 1-0 presence/absence of raw variants, counts of mutations at specific genes and counts of mutations corresponding to specific mutation signatures etc.
`Ynew`	the actual cancer categories for the test samples. This is not used in computation, but is return as a component in the output, for possibly easier post-processing.