R: Fit relaxed part of lasso model

glmnetr {glmnetr}

R Documentation

Fit relaxed part of lasso model

Description

Derive the relaxed lasso fits and optionally calls glmnet() to derive the fully penalized lasso fit.

Usage

glmnetr(
  xs_tmp,
  start_tmp,
  y_tmp,
  event_tmp,
  family = "cox",
  lambda = NULL,
  gamma = c(0, 0.25, 0.5, 0.75, 1),
  object = NULL,
  track = 0,
  ties = "efron",
  time = NULL,
  ...
)

Arguments

`xs_tmp`	predictor (X) matrix
`start_tmp`	start time in case Cox model and (Start, Stop) time for use in model
`y_tmp`	outcome (Y) variable, in case of Cox model (stop) time
`event_tmp`	event variable in case of Cox model
`family`	model family, "cox", "binomial" or "gaussian" (default)
`lambda`	lambda vector, as in glmnet(), default is NULL
`gamma`	gamma vector, as with glmnet(), default c(0,0.25,0.50,0.75,1)
`object`	an output object from glmnet() using relax=FALSE with the model fits for the fully penalized lasso models, i.e. gamma=1. Default is NULL in which case these are derived within the function.
`track`	Indicate whether or not to update progress in the console. Default of 0 suppresses these updates. The option of 1 provides these updates. In fitting clinical data with non full rank design matrix we have found some R-packages to take a vary long time or possibly get caught in infinite loops. Therefore we allow the user to track the package and judge whether things are moving forward or if the process should be stopped.
`ties`	method for handling ties in Cox model for relaxed model component. Default is "efron", optionally "breslow". For penalized fits "breslow" is always used as in the 'glmnet' package.
`time`	track progress by printing to console elapsed and split times. Suggested to use track option instead as time options will be eliminated.
`...`	Additional arguments that can be passed to glmnet()

Value

A list with two matrices, one for the model coefficients with gamma=1 and the other with gamma=0.

Examples


set.seed(82545037)
sim.data=glmnetr.simdata(nrows=200, ncols=100, beta=NULL)
xs=sim.data$xs 
y_=sim.data$yt
event=sim.data$event
glmnetr.fit = glmnetr( xs, NULL, y_, event, family="cox")
plot(glmnetr.fit)