cv.glmSparseNet {glmSparseNet}R Documentation

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

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

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')

[Package glmSparseNet version 1.0.0 Index]