ctsem (version 3.11.0)

ctEmpiricalBayesFit: 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,
  ...
)

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.

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.

Examples

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