log_likelihood | R Documentation |
Evaluate the log liklihood of parameters given observed data. Can be applied to PK or PK-PD models.
log_likelihood(lpars, pkmod, inf, tms, obs)
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) |
Numeric value of length 1
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)
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