## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
options(rmarkdown.html_vignette.check_title = FALSE)

## ----install1, eval = FALSE---------------------------------------------------
# install.packages("psbcSpeedUp")

## ----install2, eval = FALSE---------------------------------------------------
# # install.packages("remotes")
# remotes::install_github("ocbe-uio/psbcSpeedUp")

## ----results='hide', warning=FALSE--------------------------------------------
# # Load the example dataset
# data("exampleData", package = "psbcSpeedUp")
# p <- exampleData$p
# q <- exampleData$q
# survObj <- exampleData[1:3]
# 
# # Set hyperparameters (see help file for specifying more hyperparameters)
# mypriorPara <- list(
#   "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 = 1000, burnin = 500, outFilePath = "/tmp"
# )

## ----fig.width=5, fig.height=8------------------------------------------------
# plot(fitBayesCox)

## ----eval=FALSE, echo=FALSE---------------------------------------------------
# png("estimate_beta.png", bg = "transparent", width = 700, height = 900, res = 200)
# plot(fitBayesCox)
# dev.off()

## ----fig.width=6, fig.heigh=5-------------------------------------------------
# plotBrier(fitBayesCox, times = 80)

## ----eval=FALSE, echo=FALSE---------------------------------------------------
# png("estimate_brier.png", bg = "transparent", width = 1000, height = 700, res = 200)
# plotBrier(fitBayesCox, times = 80)
# dev.off()

## -----------------------------------------------------------------------------
# predict(fitBayesCox, type = c("cumhazard", "survival"))