ctmaOptimizeInit: ctmaOptimizeInit

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

ctmaOptimizeInit

Description

Initial fitting (i.e., applies ctmaInit) to a primary study reFit times to capitalize on chance for obtaining a hard-to-find optimal fit. This could be very helpful if a primary 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 ctmaOptimizeInit is like gambling, hoping that at leas one set of starting values (the number is tries is specified in the reFits argument) eneables finding the global optimal fit. On unix-like machines (e.g. MacOS), this could be done in parallel mode if coresToUse > 1.

Usage

ctmaOptimizeInit(
  primaryStudies = NULL,
  activeDirectory = NULL,
  problemStudy = NULL,
  reFits = NULL,
  finishsamples = NULL,
  n.latent = NULL,
  coresToUse = c(1),
  indVarying = FALSE,
  randomScaleTime = c(1, 1),
  activateRPB = FALSE,
  checkSingleStudyResults = FALSE,
  customPar = FALSE,
  T0means = 0,
  manifestMeans = 0,
  manifestVars = NULL,
  CoTiMAStanctArgs = NULL,
  scaleTime = 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
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. Useful to check estimates before they are saved.
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.
manifestVars	define the error variances of the manifests with a single time point using R-type lower triangular matrix with nrow=n.manifest & ncol=n.manifest.
CoTiMAStanctArgs	parameters that can be set to improve model fitting of the ctStanFit Function
scaleTime	scale time (interval) - sometimes desirable to improve fitting

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 <- ctmaOptimizeInit(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.