ctmaAllInvFit: ctmaAllInvFit

Description

#' @description Fit a CoTiMA model with all params (drift, T0var, diffusion) invariant across primary studies

Usage

ctmaAllInvFit(
  ctmaInitFit = NULL,
  activeDirectory = NULL,
  activateRPB = FALSE,
  digits = 4,
  drift = drift,
  coresToUse = c(1),
  n.manifest = 0,
  indVarying = FALSE,
  scaleTime = NULL,
  optimize = TRUE,
  nopriors = TRUE,
  priors = FALSE,
  finishsamples = NULL,
  iter = NULL,
  chains = NULL,
  verbose = NULL,
  loadAllInvFit = c(),
  saveAllInvFit = c(),
  silentOverwrite = FALSE,
  customPar = FALSE,
  T0means = 0,
  manifestMeans = 0,
  CoTiMAStanctArgs = NULL,
  lambda = NULL,
  manifestVars = NULL,
  indVaryingT0 = TRUE
)

Value

returns a fitted CoTiMA object, in which all drift parameters, Time 0 variances and covariances, and diffusion parameters were set invariant across primary studies

Arguments

ctmaInitFit: ctmaInitFit
activeDirectory: activeDirectory
activateRPB: activateRPB
digits: digits
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)
coresToUse: coresToUse
n.manifest: Number of manifest variables of the model (if left empty it will assumed to be identical with n.latent).
indVarying: Allows ct intercepts to vary at the individual level (random effects model, accounts for unobserved heterogeneity)
scaleTime: scaleTime
optimize: optimize
nopriors: nopriors (TRUE, but deprecated)
priors: priors (FALSE)
finishsamples: finishsamples
iter: iter
chains: chains
verbose: verbose
loadAllInvFit: loadAllInvFit
saveAllInvFit: saveAllInvFit
silentOverwrite: silentOverwrite
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
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 lower triangular matrix with nrow=n.manifest & ncol=n.manifest.
indVaryingT0: Allows ct intercepts to vary at the individual level (random effects model, accounts for unobserved heterogeneity)