cv.glmSparseNet {glmSparseNet} | R Documentation |
network parameter accepts:
cv.glmSparseNet( xdata, ydata, network, network.options = networkOptions(), experiment.name = NULL, ... )
xdata |
input data, can be a matrix or MultiAssayExperiment |
ydata |
response data compatible with glmnet |
network |
type of network, see below |
network.options |
options to calculate network |
experiment.name |
Name of experiment in MultiAssayExperiment |
... |
parameters that cv.glmnet accepts |
* string to calculate network based on data (correlation, covariance) * matrix representing the network * vector with already calculated penalty weights (can also be used directly glmnet)
an object just as cv.glmnet
# Gaussian model xdata <- matrix(rnorm(500), ncol = 5) cv.glmSparseNet(xdata, rnorm(nrow(xdata)), 'correlation', family = 'gaussian') cv.glmSparseNet(xdata, rnorm(nrow(xdata)), 'covariance', family = 'gaussian') # # # Using MultiAssayExperiment with survival model # # load data xdata <- MultiAssayExperiment::miniACC # # build valid data with days of last follow up or to event event.ix <- which(!is.na(xdata$days_to_death)) cens.ix <- which(!is.na(xdata$days_to_last_followup)) xdata$surv_event_time <- array(NA, nrow(colData(xdata))) xdata$surv_event_time[event.ix] <- xdata$days_to_death[event.ix] xdata$surv_event_time[cens.ix] <- xdata$days_to_last_followup[cens.ix] # # Keep only valid individuals valid.ix <- as.vector(!is.na(xdata$surv_event_time) & !is.na(xdata$vital_status) & xdata$surv_event_time > 0) xdata.valid <- xdata[, rownames(colData(xdata))[valid.ix]] ydata.valid <- colData(xdata.valid)[,c('surv_event_time', 'vital_status')] colnames(ydata.valid) <- c('time', 'status') # cv.glmSparseNet(xdata.valid, ydata.valid, nfolds = 5, family = 'cox', network = 'correlation', experiment.name = 'RNASeq2GeneNorm')