saemixControl: List of options for running the algorithm SAEM

Description Usage Arguments Details Author(s) References See Also Examples

View source: R/SaemixObject.R

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

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saemixControl(map = TRUE, fim = TRUE, ll.is = TRUE, ll.gq = FALSE,
  nbiter.saemix = c(300, 100), nb.chains = 1, fix.seed = TRUE,
  seed = 23456, nmc.is = 5000, nu.is = 4, print.is = FALSE,
  nbdisplay = 100, displayProgress = TRUE, nbiter.burn = 5,
  nbiter.mcmc = c(2, 2, 2), 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)

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 TRUE

nbiter.burn

nb of iterations for burning

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 <[email protected]>, Audrey Lavenu, Marc Lavielle.

References

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

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

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

See Also

SaemixData,SaemixModel, SaemixObject, saemix

Examples

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

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

belhal/saemix documentation built on Nov. 16, 2018, 8:30 p.m.