dot-glmSparseNetPrivate: Calculate GLM model with network-based regularization

In glmSparseNet: Network Centrality Metrics for Elastic-Net Regularized Models

Description

Calculate GLM model with network-based regularization

Usage

.glmSparseNetPrivate(
  fun,
  xdata,
  ydata,
  network,
  experiment.name = NULL,
  network.options = networkOptions(),
  ...
)

Arguments

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

Value

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)

glmSparseNet documentation built on April 14, 2021, 6 p.m.