plotBrier {psbcSpeedUp}

Time-dependent Brier scores

Description

Predict time-dependent Brier scores based on Cox regression models

Usage

plotBrier(object, survObj.new = NULL, method = "mean", times = NULL, ...)

Arguments

object

fitted object obtained with psbcSpeedUp

survObj.new

a list containing observed data from new subjects with components t, di, x. If NULL, the prediction is based on the training data

method

option to use the posterior mean ("mean") of coefficients for prediction or Bayesian model averaging ("BMA") for prediction

times

maximum time point to evaluate the prediction

...

not used

Details

psbcSpeedUp

Examples

# Load the example dataset
data("exampleData", package = "psbcSpeedUp")
p <- exampleData$p
q <- exampleData$q
survObj <- exampleData[1:3]

# Set hyperparameters
mypriorPara <- list(
  "groupInd" = 1:p, "eta0" = 0.02, "kappa0" = 1, "c0" = 2, "r" = 10 / 9,
  "delta" = 1e-05, "lambdaSq" = 1, "sigmaSq" = runif(1, 0.1, 10),
  "beta.prop.var" = 1, "beta.clin.var" = 1)


# run Bayesian Lasso Cox
library("psbcSpeedUp")
set.seed(123)
fitBayesCox <- psbcSpeedUp(survObj,
  p = p, q = q, hyperpar = mypriorPara,
  nIter = 10, burnin = 0, outFilePath = tempdir()
)
# predict survival probabilities of the train data
plotBrier(fitBayesCox, times = 80)