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 4.1-2 Index]