.glmSparseNetPrivate {glmSparseNet} | R Documentation |
Calculate GLM model with network-based regularization
.glmSparseNetPrivate(fun, xdata, ydata, network, experiment.name = NULL, network.options = networkOptions(), ...)
fun |
function to be called (glmnet or cv.glmnet) |
xdata |
input data, can be a matrix or MultiAssayExperiment |
ydata |
response data compatible with glmnet |
network |
type of network, see below |
experiment.name |
when xdata is a MultiAssayExperiment object this parameter is required |
network.options |
options to calculate network |
... |
parameters that glmnet accepts |
an object just as glmnet network parameter 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)