log_posterior_neg {tci}

Evaluate the negative log posterior value of a parameter vector

Description

Evaluate the negative log posterior value of a parameter vector given a set of observations and prior distribution for log-normally distributed PK or PK-PD parameters.

Usage

log_posterior_neg(lpars, pkmod, inf, tms, obs)

Arguments

lpars

Logged parameter values. Can be a subset of the full set of PK or PK-PD parameter values.

pkmod

'pkmod' object. Mean values are a subset of log(pars_pk), log(pars_pd), log(sigma_add), log(sigma_mult). PK-PD parameter values not specified in 'lpars' will be inferred from 'pkmod'.

inf

Infusion schedule

tms

Times associated with observations

obs

Observed values (concentrations or PD response values)

Value

Numeric value of length 1

Examples

# evaluate negative log posterior at a new set of parameters
lpars = log(c(cl=11,q2=3,q3=25,v=20,v2=40,v3=80,ke0=1.15,sigma_add=0.3))
prior_vcov <- matrix(diag(c(0.265,0.346,0.209,0.610,0.565,0.597,0.702,0.463)),
8,8, dimnames = list(NULL,names(lpars)))
my_mod <- pkmod(pars_pk = c(cl = 10, q2 = 2, q3 =20, v = 15, v2 = 30, v3 = 50,
ke0 = 1.2), sigma_add = 0.2, log_response = TRUE, Omega = prior_vcov)
inf <- inf_manual(inf_tms = 0, inf_rate = 80, duration = 2)
tms <- c(1,2,4,8,12)
obs <- simulate(my_mod, inf = inf, tms = tms)
log_posterior_neg(lpars, my_mod, inf, tms, obs)

# evaluate log-prior for subset of parameters (remove volume parameters)
lpars_sub = log(c(cl=11,q2=3,q3=25,ke0=1.15,sigma_add=0.15))
my_mod <- update(my_mod, Omega = matrix(diag(c(0.265,0.346,0.209,0.702,0.463)),
5,5, dimnames = list(NULL,names(lpars_sub))))
log_posterior_neg(lpars_sub, my_mod, inf, tms, obs)

# add a pd response and replace multiplicative error with additive error
# evaluate likelihood at new parameters
lpars_pd_eval = log(c(cl=11,q3=25,v=15,ke0=1.15,sigma_add=4,c50=5,gamma=1))
prior_vcov_pd <- matrix(diag(c(0.265,0.209,0.610,0.702,0.230,0.242,0.1)),7,7,
dimnames = list(NULL,names(lpars_pd_eval)))
my_mod_pd <- update(my_mod, pars_pd = c(c50 = 2.8, gamma = 1.47, e0 = 93, emx = 93),
pdfn = emax, pdinv = emax_inv, ecmpt = 4, sigma_mult = 0, sigma_add = 4,
Omega = prior_vcov_pd)
# simulate observations
obs_pd <- simulate(my_mod_pd, inf = inf, tms = seq(0,12,0.5))
log_posterior_neg(lpars_pd_eval, my_mod_pd, inf, tms, obs_pd)