cv_survclust: performs cross validation on supervised clustering,...

In arorarshi/survClust: Identification Of Clinically Relevant Genomic Subtypes Using Outcome Weighted Learning

performs cross validation on supervised clustering, survClust for a particular k. cv_survclust runs

Description

cv_survclust performs k fold cross-validation, runs survClust on each training and hold out test fold and return cross-validated supervised cluster labels.

Usage

cv_survclust(datasets, survdat = NULL, k, fold, cmd.k = NULL, type = NULL)

Arguments

datasets	A list object containing m data matrices representing m different genomic data types measured in a set of N~m samples. OR MultiAssayExperiment object of desired types of data. For list of matrices, each matrix, the rows represent samples, and the columns represent genomic features. Each data matrix is allowed to have different samples
survdat	A matrix, containing two columns - 1st column time and 2nd column containing events information. OR this information can be provided as a part of colData MultiAssayExperiment
k	integer, choice of k to perform clustering on samples
fold	integer, number of folds to run cross validation
cmd.k	integer, number of dimensions used by cmdscale to perform clustering on samples. Defaults is n-1
type	Specify type="mut", if datasets is of length 1 and contains binary data only.

Value

• cv.labelsreturns cross validated class labels for k cluster

• cv.logranklogrank test statistic of cross validated label

• cv.spwssstandardized pooled within-cluster sum of squares calculated from cross-validation class labels

Author(s)

Arshi Arora

Examples

library(survClust)
cv.fit <- cv_survclust(datasets = simdat, survdat = simsurvdat, k = 3, fold=3 )

arorarshi/survClust documentation built on April 21, 2024, 1:51 p.m.