optimRegCtModel: optimRegCtModel

In jhorzek/regmx:

optimRegCtModel

Description

Note: regmx is based on the R package regsem. Because of the early status of regmx, it is recommended to use regsem instead! optimRegCtModel creates a range of regularized models from a ctsem. It automatically tests different penalty values and returns the best model

Usage

optimRegCtModel(
  ctsemModelObject,
  alpha = 1,
  gamma = 0,
  regOn,
  regIndicators,
  link = list("ident"),
  dt = NULL,
  regValues,
  criterion = "BIC",
  autoCV = FALSE,
  Boot = FALSE,
  manualCV = NULL,
  k = 5,
  zeroThresh = 0.001,
  scaleCV = TRUE,
  scaleFactors = scaleFactors,
  cores = 1
)

Arguments

ctsemModelObject	an already run ctsem object
alpha	alpha controls the type of penalty. For lasso regularization, set alpha = 1, for ridge alpha = 0. Values between 0 and 1 implement elastic net regularization
gamma	gamma sets the power in the denominator of parameter specific weights when using adaptive lasso regularization. Make sure to set alpha to 1 when using a gamma other than 0.
regOn	string vector with matrices that should be regularized. The matrices must have the same name as the ones provided in the mxModelObject (e.g., "A")
regIndicators	list of matrices indicating which parameters to regularize in the matrices provided in regOn. The matrices in regIndicators must to have the same names as the matrices they correspond to (e.g., regIndicators = list("A" = diag(10))). 1 Indicates a parameter that will be regularized, 0 an unregularized parameter
link	list with functions that will be applied to the regularized matrices. For example, if the discrete time autoregressive and cross-lagged parameters should be regularized, use list("DRIFT" = "expm"). You can also define your own link function. DESCRIPTION NOT YET IMPLEMENTED
dt	list with vectors depicting the time points for which the discrete time parameters should be regularized (e.g., list("DRIFT" = c(1)))
criterion	Criterion for chosing the best final model. Possible are: AIC, BIC, CV.m2LL (only if manualCV is provided)
autoCV	logical indicating if cross-validation should be performed automatically
Boot	logical indicating if Bootstrap should be performed. Not yet implemented!
manualCV	if cross-validation should be performed manually, provide a cross-validation sample (has to be of the same class as the data in the mxModelObject; e.g., mxData)
k	number of splits performed when autoCV = TRUE
zeroThresh	Threshold at which parameters are evaluated as being zero (necessary for AIC and BIC)
scaleCV	indicate if the CV samples should be scaled automatically
cores	number of cores
regValue	numeric value depicting the penalty size
regValue_start	initial penalty value (recommended: 0)
regValue_end	highest penalty value tested
regValue_stepsize	increase in penValue between iterations

Author(s)

Jannik Orzek

Examples

library(ctsem)

set.seed(175446)

## define the population model:

# set the drift matrix. Note that drift eta_1_eta2 is set to equal 0 in the population.
ct_drift <- matrix(c(-.3,.2,0,-.5), ncol = 2)

generatingModel<-ctModel(Tpoints=10,n.latent=2,n.TDpred=0,n.TIpred=0,n.manifest=2,
                         MANIFESTVAR=diag(0,2),
                         LAMBDA=diag(1,2),
                         DRIFT=ct_drift,
                         DIFFUSION=matrix(c(.5,0,0,.5),2),
                         CINT=matrix(c(0,0),nrow=2),
                         T0MEANS=matrix(0,ncol=1,nrow=2),
                         T0VAR=diag(1,2))

# simulate a training data set
traindata <- ctGenerate(generatingModel,n.subjects = 100)

## Build the analysis model. Note that drift eta1_eta2 is freely estimated
# although it is 0 in the population.

myModel <- ctModel(Tpoints=10,n.latent=2,n.TDpred=0,n.TIpred=0,n.manifest=2,
                   LAMBDA=diag(1,2),
                   MANIFESTVAR=diag(0,2),
                   CINT=matrix(c(0,0),nrow=2),
                   DIFFUSION=matrix(c('eta1_eta1',0,0,'eta2_eta2'),2),
                   T0MEANS=matrix(0,ncol=1,nrow=2),
                   T0VAR=diag(1,2))

# fit the model using ctsem:
fit_myModel <- ctFit(traindata, myModel)
# discrete time parameters for deltaT = 1
expm(fit_myModel$mxobj$DRIFT$values)

# select DRIFT values:
regOn = "DRIFT"
regIndicators = list("DRIFT" = matrix(c(0,1,0,0), byrow = T, ncol = 2))

# regularize discrete time parameters for deltaT = 1
link = list("DRIFT" = "expm")
dt = list("DRIFT"= 1)

# optimize regularized ctsem

myRegCtModel <- optimRegCtModel(ctsemModelObject = fit_myModel, ralpha = 0, gamma = 1,
                                regOn = regOn, regIndicators = regIndicators,
                                link = link, dt = dt, autoCV = F, criterion = "BIC")

# extract discrete time parameters for deltaT = 1
expm(myRegCtModel$bestmodel$Submodel$DRIFT$values)

# optimize regularized ctsem with cross-validation

CV_myRegCtModel <- optimRegCtModel(ctsemModelObject = fit_myModel, alpha = 0, gamma = 1,
                                   regOn = regOn, regIndicators = regIndicators,
                                   link = link, dt = dt, autoCV = T, k = 5, regValue_start = 0, regValue_end = .5, regValue_stepsize = .01)

# extract discrete time parameters for deltaT = 1
expm(CV_myRegCtModel$`final Model`$Submodel$DRIFT$values)

jhorzek/regmx documentation built on Sept. 19, 2022, 2:30 a.m.