remix {REMixed} | R Documentation |
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
See |
y |
initial condition of the mechanism model, conform to what is asked in dynFUN. |
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 Both
|
alpha |
named list of named vector " |
lambda |
penalization parameter |
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 |
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 ( |
n |
number of points for gaussian quadrature (see |
parallel |
logical, if the computation should be done in parallel when possible (default TRUE). |
ncores |
number of cores for parallelization (default NULL and |
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 loglikelihoodLL.pen
values, final population parametersparam
and final regularization parametersalpha
values, number of iterationsiter
andtime
needed , if computed, the estimated standard errorsstandardError
and if test computed, the final results before testsaemBeforeTest
;iterOutputs
the list of all remix outputs, i.e. parameters, lieklihood, SAEM estimates and convergence criterion value over the iteration.
See Also
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)