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, stiff = 1, transitAbs =
0), distribution = c("normal", "poisson", "binomial"), 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, stiff = 1, transitAbs = 0),
distribution = c("normal", "poisson", "binomial"), 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, stiff =
1, transitAbs = 0), distribution = c("normal", "poisson", "binomial"),
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, stiff =
1, transitAbs = 0), distribution = c("normal", "poisson", "binomial"),
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, stiff =
1, transitAbs = 0), distribution = c("normal", "poisson", "binomial"),
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, stiff =
1, transitAbs = 0), distribution = c("normal", "poisson", "binomial"),
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, stiff =
1, transitAbs = 0), distribution = c("normal", "poisson", "binomial"),
seed = 99)
an RxODE model or lincmt()
input data
initial values
PKpars function
pred function
Covariates in data
a list of various mcmc options
optional ODE solving options
one of c("normal","poisson","binomial")
seed for random number generator
Fit a generalized nonlinear mixed-effect model using the Stochastic Approximation Expectation-Maximization (SAEM) algorithm