ctmaOptimizeFit: ctmaOptimizeFit

In CoTiMA: Continuous Time Meta-Analysis ('CoTiMA')

ctmaOptimizeFit

Description

Replaces deprecated ctmaOptimizeInit, which was limited to initial fitting (i.e., applies ctmaInit) of a primary study reFits times to capitalize on chance for obtaining a hard-to-find optimal fit. Now, optimizing a CoTiMA model generated with ctmaFit can also be done. Using ctmaOptimizeFit could be helpful if a model yields out-of-range estimates, which could happen if the fitting algorithm unfortunately used random start values that resulted in a locally but not globally optimal fit. Essentially, using ctmaOptimizeFit is like gambling, hoping that at least one set of starting values (the number it tries is specified in the reFits argument) enables finding the global optimal fit.

Usage

ctmaOptimizeFit(
  activateRPB = FALSE,
  activeDirectory = NULL,
  coresToUse = c(2),
  CoTiMAStanctArgs = NULL,
  ctmaFitFit = NULL,
  ctmaInitFit = NULL,
  customPar = FALSE,
  finishsamples = NULL,
  iter = 5000,
  primaryStudies = NULL,
  problemStudy = NULL,
  randomPar = FALSE,
  randomScaleTI = FALSE,
  randomScaleTime = c(1, 1),
  saveModelFits = FALSE,
  shuffleStudyList = FALSE,
  reFits = NULL,
  scaleTime = NULL,
  scaleTI = NULL,
  verbose = 1
)

Arguments

activateRPB	set to TRUE to receive push messages with 'CoTiMA' notifications on your phone
activeDirectory	activeDirectory
coresToUse	if neg., the value is subtracted from available cores, else value = cores to use
CoTiMAStanctArgs	parameters that can be set to improve model fitting of the ctStanFit Function
ctmaFitFit	a object fitted with ctmaFit
ctmaInitFit	the ctmaInitFit object that was used to create the ctmaFitFit object with ctmaFit
customPar	logical. If set TRUE leverages the first pass using priors and ensure that the drift diagonal cannot easily go too negative (helps since ctsem > 3.4)
finishsamples	number of samples to draw (either from hessian based covariance or posterior distribution) for final results computation (default = 1000).
iter	number of iterations (default = 5000)
primaryStudies	list of primary study information created with ctmaPrep or ctmaFitToPrep
problemStudy	number (position in list) where the problem study in primaryStudies is found
randomPar	logical (default = FALSE). Overrides arguments used for customPar and randomly sets customPar either TRUE or FALSE
randomScaleTI	logical (default = FALSE). Overrides arguments used for scaleTI and randomly sets scaleTI either TRUE or FALSE
randomScaleTime	lower and upper limit (default = c(1,1)) of uniform distribution from which timeScale argument for ctmaInit is uniformly shuffled (integer)
saveModelFits	save the fit of each Fit attempt (default = FALSE).
shuffleStudyList	(default = FALSE) randomly re-arranges studies in primaryStudyList. We encountered a few cases where this mattered, even though it should not. Only works if ctmaFit is optimized.
reFits	how many reFits should be done
scaleTime	scale time (interval) - sometimes desirable to improve fitting
scaleTI	scale TI predictors - not recommended until version 0.5.3.1. Does not change aggregated results anyways, just interpretation of effects for dummies representing primary studies.
verbose	integer from 0 to 2. Higher values print more information during model fit – for debugging

Value

returns a list with bestFit (= the best fit achieved), all_minus2ll (= all -2ll values for all fitted models), and summary, which is printed if the summary function is applied to the returned object, and which shows the summary information of the ctsem model with the best fit.

Examples

## Not run: 
optimFit313 <- ctmaOptimizeFit(primaryStudies=CoTiMAstudyList_3,
                                activeDirectory="/Users/tmp/",  # adapt!
                                problemStudy=which(CoTiMAstudyList_3$studyNumbers == 313),
                                reFits=10,
                                n.latent=2)
summary(optimFit313)

## End(Not run)

CoTiMA documentation built on May 29, 2024, 11:39 a.m.