Usage
saemixControl(algorithms = c(1, 1, 1), 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
algorithms
a vector of 1s specifying which algorithms are to be run. Defaults to c(1,1,1) (respectively estimation of the Fisher Information Matrix (by linearisation), estimation of the individual parameters, and estimation of the log-likelihood by importance sampl
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.
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
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 diff
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
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