robustlmm (version 3.5.0-2)

generateRepeatedMeasuresDatasets: 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
)

Value

A list with the same structure as the return value of

generateMixedEffectDatasets.

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.

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)
  }