Fit an SAEM model
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 class '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 class '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 class '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 class '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 class '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
)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 |
Fit a generalized nonlinear mixed-effect model using the Stochastic Approximation Expectation-Maximization (SAEM) algorithm
saem fit object
Matthew Fidler & Wenping Wang
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.