Nothing
## ---- eval = FALSE-------------------------------------------------------
# library(bhrcr)
# # If you don't bother to see the step-by-step interactive
# # process of PCE estimation and generating plots
# # please set "ask = F".
# demo(fastExample, ask = F)
# # or we can run the slowExample.
# # to save your time, we have already run the MCMC in the slow example for you.
# # the demo will show you the saved results.
# demo(slowExample, ask = F)
## ----usage, eval = FALSE-------------------------------------------------
# out <- clearanceEstimatorBayes(data = data, covariates=covariates,
# seed=1234, detect.limit=40, outlier.detect = TRUE, conf.level=.95,
# niteration = 100000, burnin = 500, thin = 50,
# filename = "output.csv")
## ----summary, eval = FALSE-----------------------------------------------
# library(bhrcr)
# data(pursat)
# data(pursat_covariates)
# results <- clearanceEstimatorBayes(data = pursat,
# covariates = pursat_covariates, seed = 1234,
# detect.limit = 15, burnin=50, niteration=100, thin=10)
# summary(results)
## ----summary result, eval = FALSE----------------------------------------
# Summary:
#
# clearanceEstimatorBayes(data = pursat, covariates = pursat_covariates,
# seed = 1234, detect.limit = 15, niteration = 100, burnin = 50, thin = 10)
#
# Posterior Estimates and Intervals for the Effect of Covariates on log half-lives
#
# Mean Median CI 2.5% CI 97.5%
# (Intercept) 1.1371 1.2486 0.3096 1.7616
# SexM 0.1648 0.1508 0.0755 0.3060
# agegroup21+ -0.0002 0.0163 -0.0674 0.0866
# vvkvTRUE -0.0227 -0.0295 -0.0985 0.0567
# HbE 0.0898 0.0961 -0.0201 0.2017
# athal -0.0348 -0.0608 -0.1263 0.1307
# g6pd -0.0168 -0.0222 -0.0814 0.0579
# lnPf0 0.0356 0.0175 -0.0140 0.1162
# year2010TRUE 0.0465 0.0488 -0.0306 0.1213
# group 0.1532 0.1522 0.0734 0.2418
# ---
# Detect Limit: 15 , Log Base: 2.718
## ----diagnostics, eval = FALSE-------------------------------------------
# # We use the results given by our previous example
# # All diagnostic plots are saved under "./mcmcDiagnostics"
# diagnostics(results)
## ----slowExample, eval = FALSE-------------------------------------------
# demo(slowExample, ask = F)
## ----plot, eval = FALSE--------------------------------------------------
# # All plots are saved under "./plots"
# plot(results)
## ----patient1, echo = FALSE, fig.align = "center"------------------------
knitr::include_graphics("./figures/patient_1.pdf")
## ----patient81, echo = FALSE, fig.align = "center"-----------------------
knitr::include_graphics("./figures/patient_81.pdf")
## ---- eval = FALSE-------------------------------------------------------
# # Example: Patient 1, 3, 14, 35
# id <- c(1, 3, 14, 35)
# a <- .025
# results$clearance.mean[id]
# [1] 0.10762175 0.08054074 0.08373204 0.11575772
#
# results$clearance.median[id]
# [1] 0.10802284 0.08176813 0.08487102 0.11725616
#
# # If we want to check several patient's profiles simultaneously
# CI <- apply(results$clearance.post[id, ], 1, quantile, probs=c(a, 1-a))
# colnames(CI) <- id
# CI
# 1 3 14 35
# 2.5% 0.1005186 0.07273923 0.07624411 0.09641293
# 97.5% 0.1167284 0.08816739 0.08975329 0.13476679
#
# # If we want to check only one patient's CI, for example patient id = 1
# id <- 1
# quantile(results$clearance.post[id, ], c(a, 1-a))
# 2.5% 97.5%
# 0.1005186 0.1167284
## ---- eval = FALSE-------------------------------------------------------
# # Here we focus on patient with id 1
# # The output is a vector of posterior samples of changepoint time
# # We display it in two rows here
# results$changelag.post[id, ]
# [1] 0.000000 0.000000 10.036735 1.831670 0.000000
# [5] 4.633040 1.847377 0.000000 0.000000 7.982357
## ---- eval = FALSE-------------------------------------------------------
# # id <- 81
# results$changetail.post[id, ]
# [1] 84 84 84 84 84 84 84 84 84 84
## ---- eval = FALSE-------------------------------------------------------
# results$lag.median[id]
# [1] 24.8569
## ---- eval = FALSE-------------------------------------------------------
# quantile(results$changelag.post[id, ], c(0.025, 0.975))
# 2.5% 97.5%
# 8.337287 28.843629
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