ctmaInit: ctmaInit

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

ctmaInit

Description

Fits ctsem models to each primary study in the supplied list of primary studies prepared by ctmaPrep.

Usage

ctmaInit(
  primaryStudies = NULL,
  activeDirectory = NULL,
  activateRPB = FALSE,
  checkSingleStudyResults = TRUE,
  digits = 4,
  n.latent = NULL,
  n.manifest = 0,
  lambda = NULL,
  manifestVars = NULL,
  drift = NULL,
  diff = NULL,
  indVarying = FALSE,
  saveRawData = list(),
  coresToUse = c(1),
  silentOverwrite = FALSE,
  saveSingleStudyModelFit = c(),
  loadSingleStudyModelFit = c(),
  scaleTI = NULL,
  scaleTime = NULL,
  optimize = TRUE,
  nopriors = TRUE,
  finishsamples = NULL,
  chains = NULL,
  iter = NULL,
  verbose = NULL,
  customPar = FALSE,
  doPar = 1,
  useSV = FALSE,
  experimental = FALSE,
  T0means = 0,
  manifestMeans = 0,
  CoTiMAStanctArgs = NULL,
  posLL = TRUE
)

Arguments

primaryStudies	list of primary study information created with ctmaPrep
activeDirectory	defines another active directory than the one used in ctmaPrep
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.
digits	number of digits used for rounding (in outputs)
n.latent	number of latent variables of the model (hast to be specified)!
n.manifest	number of manifest variables of the model (if left empty it will assumed to be identical with n.latent).
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.
drift	labels for drift effects. Have to be either of the character strings of the type V1toV2 (= freely estimated) or values (e.g., 0 for effects to be excluded, which is usually not recommended)
diff	labels for diffusion effects. Have to be either of the character strings of the type "diff_eta1" or "diff_eta2_eta1" (= freely estimated) or values (e.g., 0 for effects to be excluded, which is usually not recommended)
indVarying	control for unobserved heterogeneity by having randomly (inter-individually) varying manifest means
saveRawData	save (created pseudo) raw date. List: saveRawData$studyNumbers, $fileName, $row.names, col.names, $sep, $dec
coresToUse	if neg., the value is subtracted from available cores, else value = cores to use
silentOverwrite	overwrite old files without asking
saveSingleStudyModelFit	save the fit of single study ctsem models (could save a lot of time afterwards if the fit is loaded)
loadSingleStudyModelFit	load the fit of single study ctsem models
scaleTI	scale TI predictors
scaleTime	scale time (interval) - sometimes desirable to improve fitting
optimize	if set to FALSE, Stan's Hamiltonian Monte Carlo sampler is used (default = TRUE = maximum a posteriori / importance sampling) .
nopriors	if TRUE, any priors are disabled - sometimes desirable for optimization
finishsamples	number of samples to draw (either from hessian based covariance or posterior distribution) for final results computation (default = 1000).
chains	number of chains to sample, during HMC or post-optimization importance sampling.
iter	number of interation (defaul = 1000). Sometimes larger values could be required fom Bayesian estimation
verbose	integer from 0 to 2. Higher values print more information during model fit - for debugging
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)
doPar	parallel and multiple fitting if single studies. A value > 1 will fit each study doPar times in parallel mode during which no output is generated (screen remains silent). Useful to obtain best fit.
useSV	if TRUE (default=FALSE) start values will be used if provided in the list of primary studies
experimental	set TRUE to try new pairwise N function
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
posLL	logical. Allows (default = TRUE) of positive loglik (neg -2ll) values

Value

ctmaFit returns a list containing some arguments supplied, the fitted models, different elements summarizing the main results, model type, and the type of plot that could be performed with the returned object. The arguments in the returned object are activeDirectory, coresToUse, n.latent, n.manifest, and primaryStudyList. The study count is returned as n.studies, the created matrix of loadings of manifest on latent factors is returned as lambda, and a re-organized list of primary studies with some information ommited is returned as studyList. The fitted models for each primary study are found in studyFitList, which is a large list with many elements (e.g., the ctsem model specified by CoTiMA, the rstan model created by ctsem, the fitted rstan model etc.). Further results returned are emprawList (containing the pseudo raw data created), statisticsList (comprising baisc stats such as average sample size, no. of measurement points, etc.), a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef), and the paramter names internally used. The summary list, which is printed if the summary function is applied to the returned object, comprises "estimates" (the aggregated effects), possible randomEffects (not yet fully working), confidenceIntervals, the minus2ll value and its n.parameters, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="stanct" ("omx" was deprecated).

Examples

# Fit a ctsem model to all three primary studies summarized in
# CoTiMAstudyList_3 and save the three fitted models
## Not run: 
CoTiMAInitFit_3 <- ctmaInit(primaryStudies=CoTiMAstudyList_3,
                            n.latent=2,
                            checkSingleStudyResults=FALSE,
                            activeDirectory="/Users/tmp/") # adapt!
summary(CoTiMAInitFit_3)

## End(Not run)

CoTiMA documentation built on Nov. 10, 2022, 5:16 p.m.