remix: REMixed algorithm

In REMixed: Regularized Estimation in Mixed Effects Model

REMixed algorithm

Description

Regularization and Estimation in Mixed effects model.

Usage

remix(
  project = NULL,
  final.project = NULL,
  dynFUN,
  y,
  ObsModel.transfo,
  alpha,
  lambda,
  eps1 = 10^(-2),
  eps2 = 10^(-1),
  selfInit = FALSE,
  pop.set1 = NULL,
  pop.set2 = NULL,
  pop.set3 = NULL,
  prune = NULL,
  n = NULL,
  parallel = TRUE,
  ncores = NULL,
  print = TRUE,
  verbose = FALSE,
  digits = 3,
  trueValue = NULL,
  finalSAEM = FALSE,
  test = TRUE,
  max.iter = +Inf,
  p.max = 0.05
)

Arguments

project	directory of the Monolix project (in .mlxtran). If NULL, the current loaded project is used (default is NULL).
final.project	directory of the final Monolix project (default add "_upd" to the Monolix project).
dynFUN	function computing the dynamics of interest for a set of parameters. This function need to contain every sub-function that it may needs (as it is called in a foreach loop). The output of this function need to return a data.frame with time as first columns and named dynamics in other columns. It must take in input : y a named vector with the initial condition. The names are the dynamics names. parms a named vector of parameter. time vector a timepoint. See dynFUN_demo, model.clairon, model.pasin or model.pk for examples.
y	initial condition of the mechanism model, conform to what is asked in dynFUN. If regressor used in Monolix provided a named list of vector of individual initial conditions. Each vector need to be of length 1 (same for all), or exactly the numbre of individuals (range in the same order as their id).
ObsModel.transfo	list containing two lists of transformations and two vectors linking each transformations to their observation model name in the Monolix project. The list should include identity transformations and be named S and R. The two vectors should be named linkS and linkR. Both S (for the direct observation models) and linkS, as well as R (for latent process models) and linkR, must have the same length. S a list of transformations for the direct observation models. Each transformation corresponds to a variable Y_p=h_p(S_p), where the name indicates which dynamic is observed (from dynFUN); linkS a vector specifying the observation model names (that is used in the monolix project, alpha1, etc.) for each transformation, in the same order as in S; R similarly, a list of transformations for the latent process models. Although currently there is only one latent dynamic, each s_k, k\leq K transformation corresponds to the same dynamic but may vary for each Y_k observed. The names should match the output from dynFUN; linkR a vector specifying the observation model names for each transformation, in the same order as in R.
alpha	named list of named vector "alpha0", "alpha1" (all alpha1 are mandatory). The name of alpha$alpha0 and alpha$alpha1 are the observation model names from the monolix project to which they are linked (if the observations models are defined whithout intercept, alpha$alpha0 need to be set to the vector NULL).
lambda	penalization parameter \lambda.
eps1	integer (>0) used to define the convergence criteria for the regression parameters.
eps2	integer (>0) used to define the convergence criteria for the likelihood.
selfInit	logical, if the SAEM is already done in the monolix project should be use as the initial point of the algorithm (if FALSE, SAEM is automatically compute according to pop.set1 settings ; if TRUE, a SAEM through monolix need to have been launched).
pop.set1	population parameters setting for initialisation (see details).
pop.set2	population parameters setting for iterations.
pop.set3	population parameters setting for final estimation.
prune	percentage for prunning (\in[0;1]) in the Adaptative Gauss-Hermite algorithm used to compute the log-likelihood and its derivates (see gh.LL).
n	number of points for gaussian quadrature (see gh.LL).
parallel	logical, if the computation should be done in parallel when possible (default TRUE).
ncores	number of cores for parallelization (default NULL and detectCores is used).
print	logical, if the results and algotihm steps should be displayed in the console (default to TRUE).
verbose	logical, if progress bar should be printed when possible.
digits	number of digits to print (default to 3).
trueValue	-for simulation purposes- named vector of true value for parameters.
finalSAEM	logical, if a final SAEM should be launch with respect to the final selected set.
test	if Wald test should be computed at the end of the iteration.
max.iter	maximum number of iterations (default 20).
p.max	maximum value to each for wald test p.value (default 0.05).

Details

See REMixed-package for details on the model. For population parameter estimation settings, see (<https://monolixsuite.slp-software.com/r-functions/2024R1/setpopulationparameterestimationsettings>).

Value

a list of outputs of final project and through the iteration :

info

informations about the parameters (project path, regulatization and population parameter names, alpha names, value of lambda used, if final SAEM and test has been computed, parameters p.max and N) ;

finalRes

containing loglikelihood LL and penalized loglikelihood LL.pen values, final population parameters param and final regularization parameters alpha values, number of iterations iter and time needed , if computed, the estimated standard errors standardError and if test computed, the final results before test saemBeforeTest ;

iterOutputs

the list of all remix outputs, i.e. parameters, lieklihood, SAEM estimates and convergence criterion value over the iteration.

See Also

cv.remix.

Examples

## Not run: 
project <- getMLXdir()

ObsModel.transfo = list(S=list(AB=log10),
                        linkS="yAB",
                        R=rep(list(S=function(x){x}),5),
                        linkR = paste0("yG",1:5))

alpha=list(alpha0=NULL,
           alpha1=setNames(paste0("alpha_1",1:5),paste0("yG",1:5)))

y = c(S=5,AB=1000)
lambda = 382.22

res = remix(project = project,
            dynFUN = dynFUN_demo,
            y = y,
            ObsModel.transfo = ObsModel.transfo,
            alpha = alpha,
            selfInit = TRUE,
            eps1=10**(-2),
            eps2=1,
            lambda=lambda)

summary(res)

trueValue = read.csv(paste0(dirname(project),"/demoSMLX/Simulation/populationParameters.txt"))

plotSAEM(res,paramToPlot = c("delta_S_pop","phi_S_pop","delta_AB_pop"),trueValue=trueValue)

## End(Not run)

REMixed documentation built on Jan. 19, 2026, 5:07 p.m.