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))
)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.
Survival response variable, must be a Surv or
stratifySurv object.
The elasticnet mixing parameter, with \(0 \le \alpha \le 1\).
Observation weights.
Offset for the model. Default is a zero vector of length
nrow(y).
Indices of variables to be excluded from the model.
Separate penalty factors can be applied to each coefficient.
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.