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

ctmaOptimizeFit

R Documentation

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. On unix-like machines (e.g. MacOS), this could be done in parallel mode if coresToUse > 1.

Usage

ctmaOptimizeFit(
  primaryStudies = NULL,
  activeDirectory = NULL,
  problemStudy = NULL,
  reFits = NULL,
  finishsamples = NULL,
  n.latent = NULL,
  coresToUse = c(1),
  indVarying = FALSE,
  scaleTime = NULL,
  randomScaleTime = c(1, 1),
  activateRPB = FALSE,
  checkSingleStudyResults = FALSE,
  customPar = FALSE,
  T0means = 0,
  manifestMeans = 0,
  CoTiMAStanctArgs = NULL,
  CoTiMAFit = NULL,
  CoTiMAInitFit = NULL,
  randomPar = FALSE,
  posLL = TRUE,
  lambda = NULL,
  manifestVars = NULL
)

Arguments

`primaryStudies`	list of primary study information created with `ctmaPrep` or `ctmaFitToPrep`
`activeDirectory`	activeDirectory
`problemStudy`	number (position in list) where the problem study in primaryStudies is found
`reFits`	how many reFits should be done
`finishsamples`	number of samples to draw (either from hessian based covariance or posterior distribution) for final results computation (default = 1000).
`n.latent`	number of latent variables of the model (hast to be specified)!
`coresToUse`	if neg., the value is subtracted from available cores, else value = cores to use
`indVarying`	control for unobserved heterogeneity by having randomly (inter-individually) varying manifest means
`scaleTime`	scale time (interval) - sometimes desirable to improve fitting
`randomScaleTime`	lower and upper limit of uniform distribution from which timeScale argument for ctmaInit is uniformly shuffled (integer)
`activateRPB`	set to TRUE to receive push messages with 'CoTiMA' notifications on your phone
`checkSingleStudyResults`	displays estimates from single study 'ctsem' models and waits for user input to continue.
`customPar`	logical. If set TRUE (default) leverages the first pass using priors and ensure that the drift diagonal cannot easily go too negative (helps since ctsem > 3.4)
`T0means`	Default 0 (assuming standardized variables). Can be assigned labels to estimate them freely.
`manifestMeans`	Default 0 (assuming standardized variables). Can be assigned labels to estimate them freely.
`CoTiMAStanctArgs`	parameters that can be set to improve model fitting of the `ctStanFit` Function
`CoTiMAFit`	a object fitted with `ctmaFit`
`CoTiMAInitFit`	the ctmaInitFit object that was used to create the CoTiMAFit object with `ctmaFit`
`randomPar`	logical. Overrides arguments used fo customPar and randomly selects customPar either TRUE or FALSE
`posLL`	logical. Allows (default = TRUE) of positive loglik (neg -2ll) values
`lambda`	R-type matrix with pattern of fixed (=1) or free (any string) loadings.
`manifestVars`	define the error variances of the manifests within a single time point using R-type lower triangular matrix with nrow=n.manifest & ncol=n.manifest. Useful to check estimates before they are saved.

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.

Note

All but one of multiple cores are used on unix-type machines for parallel fitting

During fitting, not output is generated. Be patient.

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 Nov. 10, 2022, 5:16 p.m.