R: Get lambda max for Cox regression model

get_cox_lambda_max {glmnet}

R Documentation

Get lambda max for Cox regression model

Description

Return the lambda max value for Cox regression model, used for computing initial lambda values. For internal use only.

Usage

get_cox_lambda_max(
  x,
  y,
  alpha,
  weights = rep(1, nrow(x)),
  offset = rep(0, nrow(x)),
  exclude = c(),
  vp = rep(1, ncol(x))
)

Arguments

`x`	Input matrix, of dimension `nobs x nvars`; each row is an observation vector. If it is a sparse matrix, it is assumed to be unstandardized. It should have attributes `xm` and `xs`, where `xm(j)` and `xs(j)` are the centering and scaling factors for variable j respsectively. If it is not a sparse matrix, it is assumed to be standardized.
`y`	Survival response variable, must be a `Surv` or `stratifySurv` object.
`alpha`	The elasticnet mixing parameter, with 0 ≤ α ≤ 1.
`weights`	Observation weights.
`offset`	Offset for the model. Default is a zero vector of length `nrow(y)`.
`exclude`	Indices of variables to be excluded from the model.
`vp`	Separate penalty factors can be applied to each coefficient.

Details

This function is called by cox.path for the value of lambda max.

When x is not sparse, it is expected to already by centered and scaled. When x is sparse, the function will get its attributes xm and xs for its centering and scaling factors. The value of lambda_max changes depending on whether x is centered and scaled or not, so we need xm and xs to get the correct value.

[Package glmnet version 4.1 Index]