ctmaAllInvFit: ctmaAllInvFit

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

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,
  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
)

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
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.

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

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