ctmaFit {CoTiMA}R Documentation



Fits a ctsem model with invariant drift effects across primary studies, possible multiple moderators (but all of them of the the same type, either "cont" or "cat"), and possible cluster (e.g., countries where primary studies were conducted).


  ctmaInitFit = NULL,
  primaryStudyList = NULL,
  cluster = NULL,
  activeDirectory = NULL,
  activateRPB = FALSE,
  digits = 4,
  drift = NULL,
  invariantDrift = NULL,
  moderatedDrift = NULL,
  equalDrift = NULL,
  mod.number = NULL,
  mod.type = "cont",
  mod.names = NULL,
  indVarying = FALSE,
  coresToUse = c(1),
  scaleTI = NULL,
  scaleMod = NULL,
  scaleClus = NULL,
  scaleTime = NULL,
  optimize = TRUE,
  nopriors = TRUE,
  finishsamples = NULL,
  iter = NULL,
  chains = NULL,
  verbose = NULL,
  allInvModel = FALSE,
  customPar = FALSE,
  inits = NULL



object to which all single ctsem fits of primary studies has been assigned to (i.e., what has been returned by ctmaInit)


could be a list of primary studies compiled with ctmaPrep that defines the subset of studies in ctmaInitFit that should actually be used


vector with cluster variables (e.g., countries). Has to be set up carfully. Will be included in ctmaPrep in later 'CoTiMA' versions.


defines another active directory than the one used in ctmaInitFit


set to TRUE to receive push messages with 'CoTiMA' notifications on your phone


Number of digits used for rounding (in outputs)


labels for drift effects. Have to be either of the type 'V1toV2' or '0' for effects to be excluded.


drift labels for drift effects that are set invariant across primary studies (default = all drift effects).


labels for drift effects that are moderated (default = all drift effects)


Not enabled


which in the vector of moderator values shall be used (e.g., 2 for a single moderator or 1:3 for 3 moderators simultaneously)


'cont' or 'cat' (mixing them in a single model not yet possible)


vector of names for moderators used in output


allows continuous time intercepts to vary at the individual level (random effects model, accounts for unobserved heterogeneity)


if negative, the value is subtracted from available cores, else value = cores to use


scale TI predictors - not recommended if TI are dummies representing primary studies, which would be the usual case


scale moderator variables - FALSE (default) highly recommended for categorical moderators, TRUE highly recommended for continuous moderators


scale vector of cluster indicators - TRUE (default) yields avg. drift estimates, FALSE yields drift estimates of last cluster


scale time (interval) - sometimes desirable to improve fitting


if set to FALSE, Stan’s Hamiltonian Monte Carlo sampler is used (default = TRUE = maximum a posteriori / importance sampling) .


if TRUE, any priors are disabled – sometimes desirable for optimization


number of samples to draw (either from hessian based covariance or posterior distribution) for final results computation (default = 1000).


number of iterations (defaul = 1000). Sometimes larger values could be required fom Bayesian estimation


number of chains to sample, during HMC or post-optimization importance sampling.


integer from 0 to 2. Higher values print more information during model fit – for debugging


estimates a model with all parameters invariant (DRIFT, DIFFUSION, T0VAR) if set TRUE (defautl = FALSE)


logical. Leverages the first pass using priors and ensure that the drift diagonal cannot easily go too negative (could help with ctsem > 3.4)


vector of start values


ctmaFit returns a list containing somearguments supplied, the fitted model, 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, moderator names (mod.names), and moderator type (mod.type). Further arguments, which are just copied from the init-fit object supplied, are, n.latent, studyList, parameterNames, and statisticsList. The fitted model is 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 n.studies = 1 (required for proper plotting), data (created pseudo raw data), and a list with modelResults (i.e., DRIFT=model_Drift_Coef, DIFFUSION=model_Diffusion_Coef, T0VAR=model_T0var_Coef, CINT=model_Cint_Coef, MOD=modTI_Coeff, and CLUS=clusTI_Coeff). Possible invariance constraints are included in invariantDrift. The number of moderators simultaneously analyzed are included in ' n.moderators. The most important new results are returned as the list element "summary", which is printed if the summary function is applied to the returned object. The summary list element comprises "estimates" (the aggregated effects), possible randomEffects (not yet fully working), the minus2ll value and its n.parameters, the opt.lag sensu Dormann & Griffin (2015) and the max.effects that occur at the opt.lag, clus.effects and mod.effects, and possible warning messages (message). Plot type is plot.type=c("drift") and model.type="stanct" ("omx" was deprecated).


# Example 1. Fit a CoTiMA to all primary studies previously fitted one by one
# with the fits assigned to CoTiMAInitFit_6
CoTiMAFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6)

## Not run: 
# Example 2. Fit a CoTiMA with only 2 cross effects invariant (not the auto
# effects) to all primary studies previously fitted one by one with the fits
# assigned to CoTiMAInitFit_6
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAFullInv23Fit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                        invariantDrift=c("V1toV2", "V2toV1"))

## End(Not run)

## Not run: 
# Example 3. Fit a moderated CoTiMA
CoTiMAInitFit_6$activeDirectory <- "/Users/tmp/" # adapt!
CoTiMAMod1onFullFit_6 <- ctmaFit(ctmaInitFit=CoTiMAInitFit_6,
                                 mod.number=1, mod.type="cont",

## End(Not run)

[Package CoTiMA version 0.4.0 Index]