mu1.0o1c(p, times, dose=1, end=0.5) mu1.1o1c(p, times, dose=1) mu1.1o2c(p, times, dose=1) mu1.1o2cl(p, times, dose=1) mu1.1o2cc(p, times, dose=1) mu2.0o1c(p, times, dose=1, ind, end=0.5) mu2.1o1c(p, times, dose=1, ind) mu2.0o1cfp(p, times, dose=1, ind, end=0.5) mu2.1o1cfp(p, times, dose=1, ind)
p
| Vector of parameters. See the source file for details. |
times
| Vector of times. |
dose
| Vector of dose levels. |
ind
| Indicator whether parent drug or metabolite. |
end
| Time infusion ends. |
gnlr
and gnlmm
.
mu1.0o1c
: open zero-order one-compartment model
mu1.1o1c
: open first-order one-compartment model
mu1.1o2c
: open first-order two-compartment model (ordered)
mu1.1o2cl
: open first-order two-compartment model (ordered,
absorption and transfer equal)
mu1.1o2cc
: open first-order two-compartment model (circular)
Simultaneous models for parent drug and metabolite:
mu2.0o1c
: zero-order one-compartment model
mu2.1o1c
: first-order one-compartment model
mu2.0o1cfp
: zero-order one-compartment first-pass model
mu2.1o1cfp
: first-order one-compartment first-pass model
# set up a mean function for gnlr: mu <- function(p) mu2.0o1c(p, times, doses, ind) shape <- function(p) ind*p[1]+(1-ind)*p[2] gnlr(resp, "gamma", mu=mu, pmu=c(1.68,-1.15,-4.33,-0.15,-3.46), shape=shape, pshape=c(0.2,0.1)) # changing variance shape2 <- function(p) p[6]*log(mu2.0o1c(p, times, doses, ind)) gnlr(resp, "gamma", mu=mu, pmu=c(1.7,-1.2,-4.3,-0.1,-3.5), shape=shape2, pshape=c(-0.2,-3.5,-1.6,-1,1,0.5)) # for logged responses such as a log normal distribution: mul <- function(p) log(mu2.0o1c(p, times, doses, ind)) gnlr(log(resp), "normal", mu=mul, pmu=c(1.7,-1.2,-4.3,-0.1,-3.5), shape=shape, pshape=c(2,1), delta=1/resp)