pkpd {rmutil} | R Documentation |

Mean functions for use in fitting pharmacokineticcompartment models models.

`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

`mu2.0o2c1`

: zero-order two-compartment for parent,
one-compartment for metabolite, model

`mu2.0o2c2`

: zero-order two-compartment model for both parent and
metabolite

`mu2.1o1c`

: first-order one-compartment model

`mu2.0o1cfp`

: zero-order one-compartment first-pass model

`mu2.0o2c1fp`

: zero-order two-compartment for parent,
one-compartment for metabolite, model with first-pass

`mu2.0o2c2fp`

: zero-order two-compartment model for both parent and
metabolite with first-pass

`mu2.1o1cfp`

: first-order one-compartment first-pass model

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) mu2.0o2c1(p, times, dose=1, ind, end=0.5) mu2.0o2c2(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) mu2.0o2c1fp(p, times, dose=1, ind, end=0.5) mu2.0o2c2fp(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. |

The profile of mean concentrations for the given times and doses is returned.

J.K. Lindsey

library(repeated) times <- rep(1:20,2) dose <- c(rep(2,20),rep(5,20)) # set up a mean function for gar based on mu1.1o1c: mu <- function(p) { ka <- exp(p[2]) ke <- exp(p[3]) exp(p[2]-p[1])/(ka-ke)*(exp(-ke*times)-exp(-ka*times))} conc <- matrix(rgamma(40,2,scale=mu(log(c(1,0.3,0.2)))/2),ncol=20,byrow=TRUE) conc[,2:20] <- conc[,2:20]+0.5*(conc[,1:19]-matrix(mu(log(c(1,0.3,0.2))), ncol=20,byrow=TRUE)[,1:19]) conc <- ifelse(conc>0,conc,0.01) gar(conc, dist="gamma", times=1:20, mu=mu, preg=log(c(1,0.4,0.1)), pdepend=0.1, pshape=1) # changing variance shape <- mu gar(conc, dist="gamma", times=1:20, mu=mu, preg=log(c(0.5,0.4,0.1)), pdep=0.1, shape=shape, pshape=log(c(0.5,0.4,0.1)))

[Package *rmutil* version 1.0 Index]