| glmSparseNet {glmSparseNet} | R Documentation |
network parameter accepts:
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 to use as input in MultiAssayExperiment object (only if xdata is an object of this class) |
... |
parameters that 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 with glmnet)
an object just as glmnet
xdata <- matrix(rnorm(100), ncol = 20)
glmSparseNet(xdata, rnorm(nrow(xdata)), 'correlation', family = 'gaussian')
glmSparseNet(xdata, rnorm(nrow(xdata)), 'covariance', family = 'gaussian')
#
#
# Using MultiAssayExperiment
# load data
xdata <- MultiAssayExperiment::miniACC
# TODO aking out x indivudals missing values
# 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')
glmSparseNet(xdata.valid,
ydata.valid,
family = 'cox',
network = 'correlation',
experiment.name = 'RNASeq2GeneNorm')