saemixControl: List of options for running the algorithm SAEM

In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

List of options for running the algorithm SAEM

Description

List containing the variables relative to the optimisation algorithm. All these elements are optional and will be set to default values when running the algorithm if they are not specified by the user.

Usage

saemixControl(
  map = TRUE,
  fim = TRUE,
  ll.is = TRUE,
  ll.gq = FALSE,
  nbiter.saemix = c(300, 100),
  nbiter.sa = NA,
  nb.chains = 1,
  fix.seed = TRUE,
  seed = 23456,
  nmc.is = 5000,
  nu.is = 4,
  print.is = FALSE,
  nbdisplay = 100,
  displayProgress = FALSE,
  nbiter.burn = 5,
  nbiter.map = 5,
  nbiter.mcmc = c(2, 2, 2, 0),
  proba.mcmc = 0.4,
  stepsize.rw = 0.4,
  rw.init = 0.5,
  alpha.sa = 0.97,
  nnodes.gq = 12,
  nsd.gq = 4,
  maxim.maxiter = 100,
  nb.sim = 1000,
  nb.simpred = 100,
  ipar.lmcmc = 50,
  ipar.rmcmc = 0.05,
  print = TRUE,
  save = TRUE,
  save.graphs = TRUE,
  directory = "newdir",
  warnings = FALSE
)

Arguments

map	a boolean specifying whether to estimate the individual parameters (MAP estimates). Defaults to TRUE
fim	a boolean specifying whether to estimate the Fisher Information Matrix and derive the estimation errors for the parameters. Defaults to TRUE. The linearised approximation to the log-likelihood is also computed in the process
ll.is	a boolean specifying whether to estimate the log-likelihood by importance sampling. Defaults to TRUE
ll.gq	a boolean specifying whether to estimate the log-likelihood by Gaussian quadrature. Defaults to FALSE
nbiter.saemix	nb of iterations in each step (a vector containing 2 elements, nbiter.saemix[1] for the exploration phase of the algorithm (K1) and nbiter.saemix[2] for the smoothing phase (K2))
nbiter.sa	nb of iterations subject to simulated annealing (defaults to nbiter.saemix[1]/2, will be cut down to K1=nbiter.saemix[1] if greater than that value). We recommend to stop simulated annealing before the end of the exploration phase (nbiter.saemix[1]).
nb.chains	nb of chains to be run in parallel in the MCMC algorithm (defaults to 1)
fix.seed	TRUE (default) to use a fixed seed for the random number generator. When FALSE, the random number generator is initialised using a new seed, created from the current time. Hence, different sessions started at (sufficiently) different times will give different simulation results. The seed is stored in the element seed of the options list.
seed	seed for the random number generator. Defaults to 123456
nmc.is	nb of samples used when computing the likelihood through importance sampling
nu.is	number of degrees of freedom of the Student distribution used for the estimation of the log-likelihood by Importance Sampling. Defaults to 4
print.is	when TRUE, a plot of the likelihood as a function of the number of MCMC samples when computing the likelihood through importance sampling is produced and updated every 500 samples. Defaults to FALSE
nbdisplay	nb of iterations after which to display progress
displayProgress	when TRUE, the convergence plots are plotted after every nbdisplay iteration, and a dot is written in the terminal window to indicate progress. When FALSE, plots are not shown and the algorithm runs silently. Defaults to FALSE
nbiter.burn	nb of iterations for burning
nbiter.map	nb of iterations of the MAP kernel (4th kernel) to run at the beginning of the estimation process (defaults to nbiter.saemix[1]/10 if nbiter.mcmc[4] is more than 0) (EXPERIMENTAL, see Karimi et al. 2019 for details)
nbiter.mcmc	nb of iterations in each kernel during the MCMC step
proba.mcmc	probability of acceptance
stepsize.rw	stepsize for kernels q2 and q3 (defaults to 0.4)
rw.init	initial variance parameters for kernels (defaults to 0.5)
alpha.sa	parameter controlling cooling in the Simulated Annealing algorithm (defaults to 0.97)
nnodes.gq	number of nodes to use for the Gaussian quadrature when computing the likelihood with this method (defaults to 12)
nsd.gq	span (in SD) over which to integrate when computing the likelihood by Gaussian quadrature. Defaults to 4 (eg 4 times the SD)
maxim.maxiter	Maximum number of iterations to use when maximising the fixed effects in the algorithm. Defaults to 100
nb.sim	number of simulations to perform to produce the VPC plots or compute npde. Defaults to 1000
nb.simpred	number of simulations used to compute mean predictions (ypred element), taken as a random sample within the nb.sim simulations used for npde
ipar.lmcmc	number of iterations required to assume convergence for the conditional estimates. Defaults to 50
ipar.rmcmc	confidence interval for the conditional mean and variance. Defaults to 0.95
print	whether the results of the fit should be printed out. Defaults to TRUE
save	whether the results of the fit should be saved to a file. Defaults to TRUE
save.graphs	whether diagnostic graphs and individual graphs should be saved to files. Defaults to TRUE
directory	the directory in which to save the results. Defaults to "newdir" in the current directory
warnings	whether warnings should be output during the fit. Defaults to FALSE

Details

All the variables are optional and will be set to their default value when running saemix.

The function saemix returns an object with an element options containing the options used for the algorithm, with defaults set for elements which have not been specified by the user.

These elements are used in subsequent functions and are not meant to be used directly.

Author(s)

Emmanuelle Comets [e-mail], Audrey Lavenu, Marc Lavielle.

References

E Comets, A Lavenu, M Lavielle M (2017). Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm. Journal of Statistical Software, 80(3):1-41.

E Kuhn, M Lavielle (2005). Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis, 49(4):1020-1038.

E Comets, A Lavenu, M Lavielle (2011). SAEMIX, an R version of the SAEM algorithm. 20th meeting of the Population Approach Group in Europe, Athens, Greece, Abstr 2173.

B Karimi, M Lavielle , E Moulines E (2019). f-SAEM: A fast Stochastic Approximation of the EM algorithm for nonlinear mixed effects models. Computational Statistics & Data Analysis, 141:123-38

See Also

SaemixData,SaemixModel, SaemixObject, saemix

Examples

# All default options
saemix.options<-saemixControl()

# All default options, changing seed
saemix.options<-saemixControl(seed=632545)

saemix documentation built on May 29, 2024, 10:43 a.m.