generateRepeatedMeasuresDatasets: Generate Repeated-Measures Datasets With Structured...

In robustlmm: Robust Linear Mixed Effects Models

Generate Repeated-Measures Datasets With Structured Covariance

Description

Generate balanced repeated-measures datasets with a structured random-effect covariance.

Usage

generateRepeatedMeasuresDatasets(
  numberOfDatasetsToGenerate,
  numberOfSubjects,
  numberOfVisits,
  numberOfReplicates = 1L,
  structure = c("unstructured", "cs", "ar1", "diag"),
  marginalSd = 1,
  correlation = 0,
  trueBeta = 0,
  trueSigma = 1,
  errorGenerator = rnorm,
  randomEffectGenerator = rnorm
)

Arguments

numberOfDatasetsToGenerate	number of datasets to generate.
numberOfSubjects	number of subjects (grouping levels).
numberOfVisits	number of levels of the within-subject factor (the dimension of the random effect).
numberOfReplicates	number of replicates per subject-by-visit cell.
structure	random-effect covariance structure, one of "unstructured", "cs", "ar1", "diag".
marginalSd	marginal standard deviation(s) of the random effects, recycled to length numberOfVisits.
correlation	the structure's single correlation parameter (the common correlation for "cs", the lag-1 correlation for "ar1"); ignored for "diag".
trueBeta	the true intercept (the only fixed effect).
trueSigma	the true residual standard deviation.
errorGenerator, randomEffectGenerator	functions used to draw the errors and spherical random effects, see generateMixedEffectDatasets.

Details

Each subject is observed once per level of a within-subject factor visit (with numberOfVisits levels), optionally replicated numberOfReplicates times, giving a numberOfVisits-dimensional random effect per subject through the term (0 + visit | subject). The random-effect covariance follows the requested structure:

"unstructured"

an arbitrary covariance (the (0 + visit | subject) term).

"cs"

compound symmetry: a common correlation between all visits (cs(0 + visit | subject)).

"ar1"

autoregressive: \mathrm{Cor}(i, j) = \code{correlation}^{|i - j|} (ar1(0 + visit | subject)).

"diag"

uncorrelated visits (diag(0 + visit | subject)).

The data are simulated from the chosen covariance using the same machinery as generateMixedEffectDatasets; the returned object has the identical interface (including generateData, formula and the fitDatasets_* compatibility) and stores the structured covariance in trueTheta. Structured covariances require lme4 >= 2.0-0.

Value

A list with the same structure as the return value of generateMixedEffectDatasets.

See Also

generateMixedEffectDatasets, generateLongitudinalDatasets

Examples

  if (packageVersion("lme4") >= "2.0.0") {
    datasets <- generateRepeatedMeasuresDatasets(
        1, numberOfSubjects = 30, numberOfVisits = 3, numberOfReplicates = 4,
        structure = "cs", marginalSd = c(2, 1.5, 1.2), correlation = 0.5)
    fit <- rlmer(datasets$formula, datasets$generateData(1), method = "DASvar")
    VarCorr(fit)
  }

robustlmm documentation built on July 30, 2026, 5:11 p.m.