coxTBF: Fit Cox models using glmBayesMfp

In glmBfp: Bayesian Fractional Polynomials for GLMs

Description

A simplified formula based interface to glmBayesMfp to fit Cox models. Can return Maximum a posteriori (MAP) model, Median probability model (MPM) or Bayesian model average (BMA). Provides global empirical Bayes and AIC/BIC based model inference.

Usage

coxTBF(
  formula,
  data,
  type,
  baseline = "shrunk",
  globalEB = FALSE,
  IC = FALSE,
  sep = FALSE,
  keepModelList = FALSE,
  ...,
  overrideConfig
)

Arguments

formula	model formula with Surv object as LHS and uc or bfp variables as RHS.
data	data.frame for model variables
type	type of model to fit, one of "MAP","MPM","BMA","BMAFull"
baseline	how to calculate the baseline hazard function. "cox" uses unshrunken coefficients. "shrunk" refits baseline with shrunken coefficients (default).
globalEB	use global empirical bayes estimate of g (default=FALSE)
IC	use information criteria based model selection (default=FALSE). Either "AIC" or "BIC".
sep	estimate baseline hazard for each estimate of model coefficients (default=FALSE).
keepModelList	keep the model list returned by glmBayesMfp for MAP and MPM models (default=FALSE).
...	additional arguments to pass to glmBayesMfp
overrideConfig	replaces the the MAP model with the given configuration, which is passed to computeModels

Value

An object of S3 class TBFcox or TBFcox.sep if sep=TRUE.

glmBfp documentation built on July 2, 2020, 2:30 a.m.