ctEmpiricalBayesFit: Empirical Bayes subject-wise ctsem fits

In ctsem: Continuous Time Structural Equation Modelling

Empirical Bayes subject-wise ctsem fits

Description

Fits one ctsem model per subject using the model prior, estimates the empirical marginal distribution of the raw parameters, then fits each subject again using the resulting empirical Bayes prior.

Usage

ctEmpiricalBayesFit(
  datalong,
  model,
  subjects = "all",
  priors = TRUE,
  optimize = TRUE,
  cores = 2,
  subjectFitArgs = list(),
  Npasses = 2,
  ebUse = c("rawest", "rawposterior"),
  ebRobust = TRUE,
  ebOutlierMAD = 6,
  ebOutlierQuantiles = c(0.025, 0.975),
  ebWinsorize = TRUE,
  minsd = 1e-06,
  verbose = 0,
  progress = TRUE,
  ...
)

Arguments

datalong	Long format data containing multiple subjects.
model	Model object from ctModel. Time independent predictors are not supported. A random-effect-free copy of this model is used for the per-subject fits.
subjects	Vector of subject identifiers to fit, or 'all'.
priors	Logical. Passed to ctFit; defaults to TRUE.
optimize	Logical. Passed to ctFit; defaults to TRUE.
cores	Number of subjects to fit in parallel. Each individual subject-level ctFit call uses one core.
subjectFitArgs	Named list of additional arguments passed to each ctFit call. For optimized fits, optimcontrol$stochastic defaults to FALSE for all EB passes unless supplied here or in .... First-pass fits force optimcontrol$estonly=TRUE.
Npasses	Total number of subject-wise fitting passes. The default 2 fits once with the model prior, builds a marginal empirical Bayes prior, then fits once with that prior. Values above 2 repeatedly map the previous pass estimates back to the original raw scale, rebuild the marginal EB prior, and refit.
ebUse	'rawest' to build the empirical Bayes prior from first pass point estimates, or 'rawposterior' to pool raw posterior samples.
ebRobust	Logical. If TRUE, the empirical Bayes prior is built from robust raw summaries after outlier handling.
ebOutlierMAD	Positive numeric. Raw values farther than this many MAD-scaled deviations from the median are treated as outliers. Use Inf or NULL to disable this rule.
ebOutlierQuantiles	Length two numeric vector of lower and upper quantiles used to bound first-pass raw values, or NULL to disable.
ebWinsorize	Logical. If TRUE, outlying first-pass raw values are clamped to the outlier bounds before computing the EB prior. If FALSE, they are set to missing for EB prior construction.
minsd	Lower bound used for empirical raw SDs before model adjustment.
verbose	Integer from 0 to 2. Passed to ctFit.
progress	Logical. If TRUE, report the current EB fitting stage and overwrite a single console line with the subject fitting percentage.
...	Additional arguments passed to each ctFit call.

Value

Object of class ctEmpiricalBayesFit, containing the subject fit lists and metadata. $initialfits contains the first-pass model prior fits, $fits contains the final empirical Bayes prior fits, and $passfits contains every pass. Use summary() to compute final transformed-parameter means, SDs, covariances, correlations, and outlier diagnostics.

Examples

model <- ctModel(type='ct', manifestNames='Y1', LAMBDA=matrix(1))
eb <- ctEmpiricalBayesFit(ctstantestdat, model, cores=2,
  subjectFitArgs=list(optimcontrol=list(finishsamples=20)))
summary(eb)

ctsem documentation built on June 30, 2026, 5:07 p.m.