get_cox_lambda_max function

Get lambda max for Cox regression model

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

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 $0 \le \alpha \le 1$.
• 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.