Fit an SAEM model

Source: R/saem_fit.R

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

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")

seed

seed for random number generator

Value

saem fit object

Details

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

Author

Matthew Fidler & Wenping Wang