ctmaInit {CoTiMA}R Documentation

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,
  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 = 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 with a single time point using R-type matrix with nrow=n.manifest & ncol=n.manifest.

drift

labels for drift effects. Have to be either of the type V1toV2 or 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. Leverages the first pass using priors and ensure that the drift diagonal cannot easily go too negative (could help with ctsem > 3.4)

doPar

parallel and multiple fitting if single studies

useSV

if TRUE (default) start values will be used if provided in the list of primary studies

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)


[Package CoTiMA version 0.4.0 Index]