default.pf {penalizedclr}

Data adaptive candidate vector of penalty factors for L1 penalty in conditional logistic regression with covariates divided in blocks

Description

Computes a data adaptive vector of penalty factors for blocks of covariates by fitting a tentative penalized conditional logistic regression model. The penalty for the ith block is obtained as the inverse of the arithmetic mean of coefficient estimates for its covariates.

Usage

default.pf(
  response,
  stratum,
  penalized,
  unpenalized = NULL,
  alpha = 1,
  p = NULL,
  standardize = TRUE,
  event,
  nfolds = 10,
  type.step1,
  verbose = FALSE
)

Arguments

response

The response variable, either a 0/1 vector or a factor with two levels.

stratum

A numeric vector with stratum membership of each observation.

penalized

A matrix of penalized covariates.

unpenalized

A matrix of additional unpenalized covariates.

alpha

The elastic net mixing parameter, a number between 0 and 1. alpha=0 would give pure ridge; alpha=1 gives lasso. Pure ridge penalty is never obtained in this implementation since alpha must be positive.

p

The sizes of blocks of covariates, a numerical vector of the length equal to the number of blocks, and with the sum equal to the number of penalized covariates. If missing, all covariates are treated the same and a single penalty is applied.

standardize

Should the covariates be standardized, a logical value.

event

If response is a factor, the level that should be considered a success in the logistic regression.

nfolds

The number of folds used in cross-validation. Default is 10.

type.step1

Should the tentative model be fit on all covariates jointly (comb) or to each block separately (sep).

verbose

Logical. Should the message about the obtained penalty factors be printed?

Details

Blocks that contain covariates with large estimated coefficients will obtain a smaller penalty. If all estimated coefficients pertaining to a block are zero, the function returns a message. A tentative conditional logistic regression model is fit either to each covariates block separately (type.step1 = "sep") or jointly to all blocks (type.step1 = "comb"). Note that unpenalized = NULL is the only implemented option in this function as of now.

Value

The function returns a list containing the vector of penalty factors correspondng to different blocks.

References

Schulze G. (2017) Clinical Outcome Prediction based on Multi-Omics Data: Extension of IPF-LASSO. Master Thesis.

See Also

find.default.lambda