saem.fit: Fit an SAEM model

Description

Fit an SAEM model using either closed-form solutions or ODE-based model definitions

Usage

saem.fit(model, data, inits, PKpars = NULL, pred = NULL,
  covars = NULL, mcmc = list(niter = c(200, 300), nmc = 3, nu = c(2, 2,
  2)), ODEopt = list(atol = 1e-06, rtol = 1e-04, method = "lsoda",
  transitAbs = FALSE), distribution = c("normal", "poisson", "binomial",
  "lnorm"), seed = 99)
saem(model, data, inits, PKpars = NULL, pred = NULL, covars = NULL,
  mcmc = list(niter = c(200, 300), nmc = 3, nu = c(2, 2, 2)),
  ODEopt = list(atol = 1e-06, rtol = 1e-04, method = "lsoda", transitAbs
  = FALSE), distribution = c("normal", "poisson", "binomial", "lnorm"),
  seed = 99)
# S3 method for fit.nlmixr.ui.nlme
saem(model, data, inits, PKpars = NULL,
  pred = NULL, covars = NULL, mcmc = list(niter = c(200, 300), nmc =
  3, nu = c(2, 2, 2)), ODEopt = list(atol = 1e-06, rtol = 1e-04, method =
  "lsoda", transitAbs = FALSE), distribution = c("normal", "poisson",
  "binomial", "lnorm"), seed = 99)
# S3 method for fit.function
saem(model, data, inits, PKpars = NULL,
  pred = NULL, covars = NULL, mcmc = list(niter = c(200, 300), nmc =
  3, nu = c(2, 2, 2)), ODEopt = list(atol = 1e-06, rtol = 1e-04, method =
  "lsoda", transitAbs = FALSE), distribution = c("normal", "poisson",
  "binomial", "lnorm"), seed = 99)
# S3 method for fit.nlmixrUI
saem(model, data, inits, PKpars = NULL,
  pred = NULL, covars = NULL, mcmc = list(niter = c(200, 300), nmc =
  3, nu = c(2, 2, 2)), ODEopt = list(atol = 1e-06, rtol = 1e-04, method =
  "lsoda", transitAbs = FALSE), distribution = c("normal", "poisson",
  "binomial", "lnorm"), seed = 99)
# S3 method for fit.RxODE
saem(model, data, inits, PKpars = NULL,
  pred = NULL, covars = NULL, mcmc = list(niter = c(200, 300), nmc =
  3, nu = c(2, 2, 2)), ODEopt = list(atol = 1e-06, rtol = 1e-04, method =
  "lsoda", transitAbs = FALSE), distribution = c("normal", "poisson",
  "binomial", "lnorm"), seed = 99)
# S3 method for fit.default
saem(model, data, inits, PKpars = NULL,
  pred = NULL, covars = NULL, mcmc = list(niter = c(200, 300), nmc =
  3, nu = c(2, 2, 2)), ODEopt = list(atol = 1e-06, rtol = 1e-04, method =
  "lsoda", transitAbs = FALSE), distribution = c("normal", "poisson",
  "binomial", "lnorm"), seed = 99)

Arguments

model

an RxODE model or lincmt()

data

input data

inits

initial values

PKpars

PKpars function

pred

pred function

covars

Covariates in data

mcmc

a list of various mcmc options

ODEopt

optional ODE solving options

distribution

one of c("normal","poisson","binomial", "lnorm")

seed

seed for random number generator

Details

Fit a generalized nonlinear mixed-effect model using the Stochastic Approximation Expectation-Maximization (SAEM) algorithm