log_posterior: Log-posterior for PK parameters

In hlweeks/pkpredict: Compartment Model Implementation for Pharmacokinetic Analysis

Log-posterior for PK parameters

Description

Evaluates the prior distribution (normal) of the log parameter vector with normally distributed error.

Usage

log_posterior(
  lpr,
  ivt,
  dat,
  mu = getOption("pkpredict.pip.default.prior")$log_pk_mean,
  sig = getOption("pkpredict.pip.default.prior")$log_pk_vcov,
  ler_mean = getOption("pkpredict.pip.default.prior")$log_err_mean,
  ler_sdev = getOption("pkpredict.pip.default.prior")$log_err_sd
)

Arguments

lpr	log-PK parameter vector with error: (lv_1, lk_10, lk_12, lk_21, ler_mean)
ivt	List with containing start of infusion times (h), end of infusion times (h), and rate of infusion (g/h) at each dose
dat	Concentration data frame of the form: data.frame(time_h, conc_mg_dl)
mu	prior pk param mean
sig	prior pk vcov matrix
ler_mean	prior error mean
ler_sdev	prior error sd

Value

The log-posterior distribution evaluated at the specified log-parameter vector

Examples

ivt_d <- list(list(begin=0.0, end=0.5, k_R=6),
              list(begin=8.0, end=8.5, k_R=6),
              list(begin=16.0, end=16.5, k_R=6))
dat_d <- data.frame(time_h = c(1,4,40), conc_mcg_ml = c(82.7,80.4,60))

log_posterior(lpr = c(getOption("pkpredict.pip.default.prior")$log_pk_mean,
                      getOption("pkpredict.pip.default.prior")$log_err_mean),
              ivt = ivt_d, dat = dat_d)

hlweeks/pkpredict documentation built on Oct. 29, 2023, 6:08 a.m.