log_likelihood {tci}

Evaluate the log likelihood of a vector of parameter values

Description

Evaluate the log liklihood of parameters given observed data. Can be applied to PK or PK-PD models.

Usage

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

Arguments

lpars

Named vector of logged parameter values to be evaluated. This should include any PK or PD parameters, as well as residual error standard deviations (sigma_add or sigma_mult) that are to be evaluated.

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

my_mod <- pkmod(pars_pk = c(cl = 10, q2 = 2, q3 =20, v = 15, v2 = 30, v3 = 50,
 ke0 = 1.2), sigma_mult = 0.2)
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)
# evaluate log-likelihood at a new set of parameters
lpars = log(c(cl=11,q2=3,q3=25,v=15,v2=30,v3=50,ke0=1.15,sigma_mult=0.3))
log_likelihood(lpars, my_mod, inf, tms, obs)

# estimate for a subset of parameters (exclude q2, v2, v3)
lpars_sub = log(c(cl=11,q3=25,v=15,ke0=1.15,sigma_mult=0.3))
log_likelihood(lpars_sub, my_mod, inf, tms, obs)

# add a pd response and replace multiplicative error with additive error
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)
# simulate observations
obs_pd <- simulate(my_mod_pd, inf = inf, tms = seq(0,12,0.5))
# evaluate likelihood at new parameters
lpars_pd <- log(c(cl=11,q3=25,v=15,ke0=1.15,sigma_add=4,c50=5,gamma=1))
log_likelihood(lpars_pd, my_mod_pd, inf, tms = seq(0,12,0.5), obs_pd)