Calculate cross validating GLM model with network-based regularization

Description

network parameter accepts:

Usage

cv.glmSparseNet(xdata, ydata, network,
  network.options = networkOptions(), experiment.name = NULL, ...)

Arguments

Details

* 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)

Value

an object just as cv.glmnet

Examples

## Not run: 
    # 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')

## End(Not run)

#
#
# 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')