configsaem: Configure an SAEM model

In nlmixr: Nonlinear Mixed Effects Models in Population PK/PD

Configure an SAEM model

Description

Configure an SAEM model by generating an input list to the SAEM model function

Usage

configsaem(
  model,
  data,
  inits,
  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,
    maxeval = 1e+05),
  distribution = c("normal", "poisson", "binomial"),
  addProp = c("combined2", "combined1"),
  seed = 99,
  fixed = NULL,
  DEBUG = 0,
  tol = 1e-04,
  itmax = 100L,
  type = c("nelder-mead", "newuoa"),
  lambdaRange = 3,
  powRange = 10,
  odeRecalcFactor = 10^(0.5),
  maxOdeRecalc = 5L
)

Arguments

model	a compiled saem model by gen_saem_user_fn()
data	input data
inits	initial values
mcmc	a list of various mcmc options
ODEopt	optional ODE solving options
distribution	one of c("normal","poisson","binomial")
addProp	one of "combined1" and "combined2"; These are the two forms of additive+proportional errors supported by monolix/nonmem: combined1: transform(y)=transform(f)+(a+b*f^c)*eps combined2: transform(y)=transform(f)+(a^2+b^2*f^(2c))*eps
seed	seed for random number generator
fixed	a character vector of fixed effect only parameters (no random effects attached) to be fixed
DEBUG	Integer determining if debugging is enabled
tol	This is the tolerance for the regression models used for complex residual errors (ie add+prop etc)
itmax	This is the maximum number of iterations for the regression models used for complex residual errors. The number of iterations is itmax*number of parameters
type	indicates the type of optimization for the residuals; Can be one of c("nelder-mead", "newuoa")
lambdaRange	This indicates the range that Box-Cox and Yeo-Johnson parameters are constrained to be; The default is 3 indicating the range (-3,3)
powRange	This indicates the range that powers can take for residual errors; By default this is 10 indicating the range is c(1/10, 10) or c(0.1,10)
odeRecalcFactor	The factor to increase the rtol/atol with bad ODE solving.
maxOdeRecalc	Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.

Details

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

Value

Returns a list neede for the saem fit procedure

Author(s)

Wenping Wang & Matthew Fidler

Examples

# In this ODE system we simply specify the ODEs

ode <- "d/dt(depot) =-KA*depot;
        d/dt(centr) = KA*depot - KE*centr;"
m1 <- RxODE(ode)


# In this ode System, we also specify the concentration as C2 = centr/V

ode <- "C2 = centr/V;
      d/dt(depot) =-KA*depot;
      d/dt(centr) = KA*depot - KE*centr;"
m2 = RxODE(ode)

PKpars <- function() {
  CL <- exp(lCL)
  V <- exp(lV)
  KA <- exp(lKA)
  KE <- CL / V
}

PRED <- function() centr / V
PRED2 <- function() C2

saem_fit <- gen_saem_user_fn(model = m1, PKpars, pred = PRED)

# Can also use PRED2
saem_fit <- gen_saem_user_fn(model=m2, PKpars, pred=PRED2)


# You can also use the nlmixr UI to run this model and call the lower level functions

one.compartment <- function() {
ini({
  tka <- 0.45 # Log Ka
  tcl <- 1 # Log Cl
  tv <- 3.45    # Log V
  eta.ka ~ 0.6
  eta.cl ~ 0.3
  eta.v ~ 0.1
  add.sd <- 0.7
  wt.est <- 0.0
})
model({
  ka <- exp(tka + eta.ka)
  cl <- exp(tcl + eta.cl)
  v <- exp(tv + eta.v + wt.est * WT)
  d/dt(depot) = -ka * depot
  d/dt(center) = ka * depot - cl / v * center
  cp = center / v
  cp ~ add(add.sd)
})
}
fit  <- nlmixr(one.compartment, theo_sd, "saem")
fit

nlmixr documentation built on March 27, 2022, 5:05 p.m.