fitDatasets: Fitting Functions

In robustlmm: Robust Linear Mixed Effects Models

Fitting Functions

Description

Methods to fit various mixed effects estimators to all generated datasets.

Usage

fitDatasets_lmer(datasets, control, label, postFit, datasetIndices = "all")

fitDatasets_lmer_bobyqa(datasets, postFit, datasetIndices = "all")

fitDatasets_lmer_Nelder_Mead(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer(
  datasets,
  method,
  tuningParameter,
  label,
  postFit,
  datasetIndices = "all",
  ...,
  init
)

fitDatasets_rlmer_DAStau(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer_DAStau_lmerNoFit(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer_DASvar(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer_DAStau_noAdj(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer_DAStau_k_0_5(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer_DAStau_k_0_5_noAdj(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer_DAStau_k_2(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer_DAStau_k_2_noAdj(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer_DAStau_k_5(datasets, postFit, datasetIndices = "all")

fitDatasets_rlmer_DAStau_k_5_noAdj(datasets, postFit, datasetIndices = "all")

fitDatasets_heavyLme(datasets, postFit, datasetIndices = "all")

fitDatasets_lqmm(datasets, postFit, datasetIndices = "all")

fitDatasets_rlme(datasets, postFit, datasetIndices = "all")

fitDatasets_varComprob(
  datasets,
  control,
  label,
  postFit,
  datasetIndices = "all"
)

fitDatasets_varComprob_compositeTau(datasets, postFit, datasetIndices = "all")

fitDatasets_varComprob_compositeTau_OGK(
  datasets,
  postFit,
  datasetIndices = "all"
)

fitDatasets_varComprob_compositeTau_2SGS(
  datasets,
  postFit,
  datasetIndices = "all"
)

fitDatasets_varComprob_compositeS(datasets, postFit, datasetIndices = "all")

fitDatasets_varComprob_compositeS_OGK(
  datasets,
  postFit,
  datasetIndices = "all"
)

fitDatasets_varComprob_compositeS_2SGS(
  datasets,
  postFit,
  datasetIndices = "all"
)

fitDatasets_varComprob_S(datasets, postFit, datasetIndices = "all")

fitDatasets_varComprob_S_OGK(datasets, postFit, datasetIndices = "all")

fitDatasets_varComprob_S_2SGS(datasets, postFit, datasetIndices = "all")

Arguments

datasets	Datasets list to be used to generate datasets.
control	a list (of correct class for the respective fitting function) containing control parameters to be passed through.
label	a string used to identify which fits have been created by which function.
postFit	a function, taking one argument, the resulting fit. This makes it easy to add an additional step after fitting.
datasetIndices	optional vector of dataset indices to fit, useful to try only a few datasets instead of all of them.
method	argument passed on to rlmer.
tuningParameter	argument passed on to extractTuningParameter.
...	argument passed on to createRhoFunction.
init	optional argument passed on to rlmer.

Details

Existing fitting functions are:

fitDatasets_lmer: Fits datasets using lmer using its default options.

fitDatasets_lmer_bobyqa: Fits datasets using lmer using the bobyqa optimizer.

fitDatasets_lmer_Nelder_Mead: Fits datasets using lmer using the Nelder Mead optimizer.

fitDatasets_rlmer: Fits datasets using rlmer using a custom configuration. The argument 'tuningParameter' is passed to extractTuningParameter, details are documented there.

fitDatasets_rlmer_DAStau: Fits datasets using rlmer using method DAStau and smoothPsi for the rho functions. The tuning parameters are k = 1.345 for rho.e. For rho.sigma.e, the Proposal 2 variant is used using k = 2.28. The choices for rho.b and rho.sigma.b depend on whether the model uses a diagonal or a block diagonal matrix for Lambda. In the former case, the same psi functions and tuning parameters are use as for rho.e and rho.sigma.b. In the block diagonal case, rho.b and rho.sigma.b both use smoothPsi using a tuning parameter k = 5.14 (assuming blocks of dimension 2).

fitDatasets_rlmer_DAStau_lmerNoFit: Fits datasets using rlmer using the same configuration as fitDatasets_rlmer_DAStau except for that it is using lmerNoFit as initial estimator.

fitDatasets_rlmer_DASvar: Fits datasets using rlmer using method DASvar. The same rho functions and tuning parameters are used as for fitDatasets_rlmer_DAStau.

fitDatasets_rlmer_DAStau_noAdj: Fits datasets using rlmer using method DAStau. The same rho functions and tuning parameters are used as for fitDatasets_rlmer_DAStau, except for rho.sigma.e (and rho.sigma.b in the diagonal case) for which the Proposal 2 variant of smoothPsi using k = 1.345 is used.

fitDatasets_rlmer_DAStau_k_0_5: Fits datasets using rlmer using method DAStau. Use smoothPsi psi-function with tuning parameter k = 0.5 for rho.e and k = 1.47 for rho.sigma.e, the latter adjusted to reach the same asymptotic efficiency. In the diagonal case, the same are used for rho.b and rho.sigma.b as well. In the block-diagonal case, the tuning parameter k = 2.17 is used for rho.b and rho.sigma.b. The tuning parameter is chosen to reach about the same asymptotic efficiency for theta as for the fixed effects.

fitDatasets_rlmer_DAStau_k_0_5_noAdj: Fits datasets using rlmer using method DAStau. Use smoothPsi psi-function with tuning parameter k = 0.5 for rho.e and rho.sigma.e. In the diagonal case, the same are used for rho.b and rho.sigma.b as well. In the block-diagonal case, the tuning parameter k = 2.17 is used for rho.b and rho.sigma.b. The tuning parameter is chosen to reach about the same asymptotic efficiency for theta as for the fixed effects.

fitDatasets_rlmer_DAStau_k_2: Fits datasets using rlmer using method DAStau. Use smoothPsi psi-function with tuning parameter k = 2 for rho.e and k = 2.9 rho.sigma.e, the latter adjusted to reach the same asymptotic efficiency. In the diagonal case, the same are used for rho.b and rho.sigma.b as well. In the block-diagonal case, the tuning parameter k = 8.44 is used for rho.b and rho.sigma.b. The tuning parameter is chosen to reach about the same asymptotic efficiency for theta as for the fixed effects.

fitDatasets_rlmer_DAStau_k_2_noAdj: Fits datasets using rlmer using method DAStau. Use smoothPsi psi-function with tuning parameter k = 2 for rho.e and rho.sigma.e. In the diagonal case, the same are used for rho.b and rho.sigma.b as well. In the block-diagonal case, the tuning parameter k = 8.44 is used for rho.b and rho.sigma.b. The tuning parameter is chosen to reach about the same asymptotic efficiency for theta as for the fixed effects.

fitDatasets_rlmer_DAStau_k_5: Fits datasets using rlmer using method DAStau. Use smoothPsi psi-function with tuning parameter k = 5 for rho.e and k = 5.03 rho.sigma.e, the latter adjusted to reach the same asymptotic efficiency. In the diagonal case, the same are used for rho.b and rho.sigma.b as well. In the block-diagonal case, the tuning parameter k = 34.21 is used for rho.b and rho.sigma.b. The tuning parameter is chosen to reach about the same asymptotic efficiency for theta as for the fixed effects.

fitDatasets_rlmer_DAStau_k_5_noAdj: Fits datasets using rlmer using method DAStau. Use smoothPsi psi-function with tuning parameter k = 5 for rho.e and rho.sigma.e. In the diagonal case, the same are used for rho.b and rho.sigma.b as well. In the block-diagonal case, the tuning parameter k = 34.21 is used for rho.b and rho.sigma.b. The tuning parameter is chosen to reach about the same asymptotic efficiency for theta as for the fixed effects.

fitDatasets_heavyLme: Fits datasets using heavyLme from package heavy. Additional required arguments are: lmeFormula, heavyLmeRandom and heavyLmeGroups. They are passed to the formula, random and groups arguments of heavyLme.

fitDatasets_lqmm: Fits datasets using lqmm from package lqmm. Additional required arguments are: lmeFormula, lqmmRandom, lqmmGroup and lqmmCovariance. They are passed to the formula, random, groups and covariance arguments of lqmm. lqmmCovariance is optional, if omitted pdDiag is used.

fitDatasets_rlme: Fits datasets using rlme from package rlme.

fitDatasets_varComprob: Prototype method to fit datasets using varComprob from package robustvarComp. Additional required items in datasets are: lmeFormula, groups, varcov and lower. They are passed to the fixed, groups, varcov and lower arguments of varComprob. The running of this method produces many warnings of the form "passing a char vector to .Fortran is not portable" which are suppressed.

fitDatasets_varComprob_compositeTau: Fits datasets with the composite Tau method using varComprob from package robustvarComp. See fitDatasets_varComprob for additional details.

fitDatasets_varComprob_compositeTau_OGK: Similar to fitDatasets_varComprob_compositeTau but using covOGK as initial covariance matrix estimator.

fitDatasets_varComprob_compositeTau_2SGS: Similar to fitDatasets_varComprob_compositeTau but using 2SGS as initial covariance matrix estimator.

fitDatasets_varComprob_compositeS: Similar to fitDatasets_varComprob_compositeTau but using method composite S.

fitDatasets_varComprob_compositeS_OGK: Similar to fitDatasets_varComprob_compositeS but using covOGK as initial covariance matrix estimator.

fitDatasets_varComprob_compositeS_2SGS: Similar to fitDatasets_varComprob_compositeS but using 2SGS as initial covariance matrix estimator.

fitDatasets_varComprob_S: Similar to fitDatasets_varComprob_compositeTau but using method S and the Rocke psi-function.

fitDatasets_varComprob_S_OGK: Similar to fitDatasets_varComprob_S but using covOGK as initial covariance matrix estimator.

fitDatasets_varComprob_S_2SGS: Similar to fitDatasets_varComprob_S but using 2SGS as initial covariance matrix estimator.

Value

list of fitted models. See also lapplyDatasets which is called internally.

Author(s)

Manuel Koller

Examples

  set.seed(1)
  oneWay <- generateAnovaDatasets(1, 1, 10, 4,
                                  lmeFormula = y ~ 1,
                                  heavyLmeRandom = ~ 1,
                                  heavyLmeGroups = ~ Var2,
                                  lqmmRandom = ~ 1,
                                  lqmmGroup = "Var2",
                                  groups = cbind(rep(1:4, each = 10), rep(1:10, 4)),
                                  varcov = matrix(1, 4, 4),
                                  lower = 0)
  fitDatasets_lmer(oneWay)
  ## call rlmer with custom arguments
  fitDatasets_rlmer_custom <- function(datasets) {
    return(fitDatasets_rlmer(datasets,
                             method = "DASvar",
                             tuningParameter = c(1.345, 2.28, 1.345, 2.28, 5.14, 5.14),
                             label = "fitDatasets_rlmer_custom"))
  }
  fitDatasets_rlmer_custom(oneWay)

robustlmm documentation built on June 8, 2025, 12:20 p.m.