\documentclass[nojss]{jss}
\usepackage{rlmer}
%\VignetteIndexEntry{Replication Code For Simulation Studies}
%\VignetteDepends{ggplot2, robustlmm, MASS, skewt, reshape2, ggh4x}

\usepackage[utf8]{inputenc}
\usepackage{geometry}
\usepackage{amsmath}
\usepackage{natbib}
\usepackage[export]{adjustbox}[2011/08/13]
\usepackage{booktabs}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% declarations for jss.cls %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\author{Manuel Koller}
\title{Replication Code For Simulation Studies}
\Plaintitle{Replication Code For Simulation Studies}

\Abstract{Instructions how to replicate several simulation
    studies made to study properties of the robust scoring equations
    estimator (RSE). All the required code and the simulation results
    are included. The code is written in a way that makes sure that
    the stored simulation results are still reproduced by the current
    code.}

\Keywords{robust statistics, mixed-effects model, hierarchical model,
  ANOVA, \proglang{R}, crossed, random effect, simulation study}
\Plainkeywords{robust statistics, mixed-effects model, hierarchical model,
  ANOVA, R, crossed, random effect, simulation study}

%% The address of (at least) one author should be given
%% in the following format:
\Address{
  Manuel Koller\\
  E-mail: \email{kollerma@proton.me}
}

%% end of declarations %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%\usepackage{Sweave}

\newcommand{\script}[1]{\href{https://github.com/kollerma/robustlmm/blob/master/inst/simulationStudy/#1}{\code{#1}}}
% Same as \script but without the GitHub hyperlink, for scripts that
% ship in the package but are not yet on the public repository's master
% branch (a linked URL would 404). Switch back to \script once the file
% is published on https://github.com/kollerma/robustlmm.
\newcommand{\scriptpending}[1]{\code{#1}}

\begin{document}
\SweaveOpts{concordance=TRUE,echo=FALSE}
\setkeys{Gin}{width=1.2\textwidth}

<<basic-options,echo=FALSE>>=
options(width=70,
        str=strOptions(strict.width = "wrap", vec.len=2),
        continue = "  ")
require(ggplot2)
theme <- theme_bw(base_size = 9)
theme$legend.position = "top"
theme_set(theme)
## Headline numbers for the "new in 3.5.0" studies are precomputed from
## the simulation results into this shipped RDS (by
## vignettes/precompute-3.5.0.R) and pulled in with \Sexpr{}, so they
## always match the studies and are never hand-typed.
nums <- readRDS("robustlmm-3.5.0-numbers.rds")
fmt <- function(x, digits = 2) formatC(x, format = "f", digits = digits)
@

\baselineskip 15pt

\section{Introduction}
This is a technical document that should make it easy for others
to replicate the results of the simulation studies presented in
\citet{koller2013diss} and \citet{koller2022rse}. For the sake of
brevity, the full descriptions of the studies are not duplicated
here. Please refer to the cited document. The same goes for the
code itself. Instead of printing long listings here that are not
very useful, \pkg{robustlmm} provides a function that opens the
the corresponding script in the user's editor. All code is implemented
in \Rp \citep{R}.

In the next section, we give an overview of the included simulation
studies and how to access the code. In
Section~\ref{sec:generalStructure} we will cover the general structure
of the code that is used to run each simulation study.
Section~\ref{sec:packages} shows a list of \Rp packages that were
used in the simulation studies' code. The charts
for each simulation study are then shown in turn. Simulation studies
that have not been published elsewhere are described in detail while
others are just referenced. Section~\ref{sec:sensitivityCurves} contains the
results for the Sensitivity curves simulation study, consistency
and efficiency for varying tuning parameters of the $\psi$-functions in
Section~\ref{sec:consistencyAndEfficiency}, breakdown in
Section~\ref{sec:breakdown}, convergence for increasing number
of observations in Section~\ref{sec:convergence}, and,
robustness and empirical test coverage in
Section~\ref{sec:robustnessAndCoverage}. Finally,
Section~\ref{sec:sessionInfo} shows the output of
\code{sessionInfo()} that lists all versions of packages that
were loaded when the simulation studies were run.

\section{The code}
\label{sec:thecode}
The code for each simulation study is shipped as script file
with \pkg{robustlmm}. To see it, run a variation of following
command in \Rp or click the script name in this document to
open the file on github.

<<eval=FALSE, echo=TRUE>>=
robustlmm::viewCopyOfSimulationStudy("sensitivityCurves.R")
@

This will create a copy of the \code{sensitivityCurves.R} script
in the local working directory and open the script for editing
and running.

As CRAN does not allow built package archives to be more than 5 MB in
size, the version of the package on CRAN does not contain the full
simulation results. Instead, only aggregated simulation results are
included. A version of the package with the full results is available
on github. You can install said version using the following command.

<<eval=FALSE, echo=TRUE>>=
remotes::install_github("kollerma/robustlmm", "full-results")
@

The following simulation studies are provided:
\begin{itemize}
    \item Section~\ref{sec:sensitivityCurves}:
        \script{sensitivityCurves.R}
        reproduces Figure~4.1 in \citet{koller2013diss} and
        Figure~1.1 in \citet{koller2022rse}.
    \item Section~\ref{sec:consistencyAndEfficiency}:
        \script{consistencyAndEfficiencyDiagonal.R}
        reproduces Figure~4.2, Figure~4.3 and Figure~4.4
        in \citet{koller2013diss}.
    \item Section~\ref{sec:consistencyAndEfficiencyBlockDiagonal}:
        \script{consistencyAndEfficiencyBlockDiagonal.R}
        a previously unpublished simulation study, the analogue
        of the previous script but for the block diagonal case.
    \item Section~\ref{sec:convergence}: \script{convergence.R}
        a previously unpublished simulation study, in which the
        effect of increasing sample size is studied.
    \item Section~\ref{sec:breakdown}:
        \script{breakdown.R} reproduces Figure~4.5 in
        \citet{koller2013diss}.
    \item Section~\ref{sec:breakdownMC}:
        \script{breakdownMC.R} a Monte-Carlo extension of the
        breakdown study that compares the new RANSAC and
        redescending-$\psi$ wrappers under several contamination
        strategies per design.
    \item Section~\ref{sec:robustnessAndCoverage}:
        \script{robustnessDiagonal.R} replicates the simulation
        study in Section~4.2 in \citet{koller2022rse}
        (Figure~2 and Figure~3)
    \item Section~\ref{sec:robustnessBlockDiagonal}:
        \script{robustnessBlockDiagonal.R} replicates the
        simulation study in Section~4.4 in \citet{koller2022rse}
        (Figure~4 and Figure~5).
    \item Section~\ref{sec:new350}: the studies backing the methods new
        in the 3.5.0 release ---
        \script{inferenceStudy.R} (Section~\ref{sec:inferenceStudy}),
        \script{leverageBreakdown.R} and
        \script{leverageGES.R} (Section~\ref{sec:leverageBreakdown}),
        \script{mallowsEfficiency.R} and
        \script{mallowsEfficiencyDim.R}
        (Section~\ref{sec:mallowsEfficiency}),
        \script{ransacBasin.R} and
        \script{ransacConsensus.R}
        (Section~\ref{sec:ransacStudies}), and
        \script{structuredCovariances.R}
        (Section~\ref{sec:structuredCovariances}).
\end{itemize}

\script{consistencyAndEfficiencyBlockDiagonal.R},
\script{convergence.R} and \script{breakdownMC.R} have not been
published elsewhere, so a description of the study and a discussion
are included in this document.

\section{General structure of code for each presented study}
\label{sec:generalStructure}
All the simulation studies presented here follow the same
pattern, consisting, broadly speaking, of the following four steps:
\begin{enumerate}
    \item[\ref{sec:generationOfDatasets}] Generation of datasets which
        will be fitted using various methods,
    \item[\ref{sec:fittingEachDataset}] fitting each dataset using each
        method and extracting all relevant information from the fitted
        models,
    \item[\ref{sec:prepareResultsForPlotting}] preparing one or more
        datasets of results suitable for plotting and finally,
    \item[\ref{sec:plottingTheResults}] plotting the results.
\end{enumerate}

The \pkg{robustlmm} \Rp package provides a set of methods that
cover all of these steps and make it easy to run a simulation
study. Full documentation is available in the help for each
function. The simplest way to get started is probably by
working through one of the scripts that are provided
in \pkg{robustlmm}. Each of those scripts is discussed in
Section~\ref{sec:sensitivityCurves} and later.

We will now go though each of the four steps and provide a
few pointers how to use the provided functions.

\subsection{Generation of datasets}
\label{sec:generationOfDatasets}
The methods provided in \pkg{robustlmm} assume that the datasets
come in a specific format. Besides the datasets themselves, one
also has to provide all the information that needs to be passed
to each of the fitting functions, e.g., the formula that specifies
the model.

There are four functions available in \pkg{robustlmm}:
\begin{enumerate}
\item \code{createDatasetsFromList} converts a list of generated
    datasets into the required format.
\item \code{generateAnovaDatasets} create a ANOVA type balanced
    dataset with a variety of fixed and random grouping variables.
\item \code{generateMixedEffectDatasets} creates datasets using
    parameterized bootstrap off a base dataset that was prepared
    using \code{prepareMixedEffectDataset}.
\item \code{generateSensitivityCurveDatasets} creates datasets where
    one or more observations are changed more and more away from the
    original values in order to show how sensitive a method is to
    changes in the data.
\end{enumerate}

All the functions produce a list that contains all the ingredients
used later on. The resulting list contains functions that can
produce a dataset for a given dataset index and other functions
that are useful to combine multiple datasets together.

\code{createDatasetsFromList} is the most flexible way of creating
a dataset list, the only limitation is that one can specify only one
formula per dataset list. The trade off for the flexibility is that
\code{createDatasetsFromList} is not as memory efficient as the two
\code{generateAnovaDatasets} and \code{generateMixedEffectDatasets}
functions.

Both \code{generateAnovaDatasets} and
\code{generateMixedEffectDatasets} store as little redundant
information as possible and only generate the full dataset on
demand.

\subsection{Fitting each dataset and extracting the relevant information}
\label{sec:fittingEachDataset}
After the datasets have been simulated / generated, the next step
is to apply each of the methods to each of the datasets. Then
results need to be extracted into a memory efficient form and
stored on disk for later re-use.

For each method that should be used for fitting the datasets,
one has to provide a function that takes the dataset list
produced in the previous step as the first argument. \pkg{robustlmm}
comes with many pre-defined methods, see \code{?fitDatasets}.
These functions are essentially wrappers that prepare some arguments
needed for the fitting function and then call \code{lapplyDatasets}
which does the actual work.

The fitted objects are preferably not stored on disk as these
are usually very large when saved. To reduce the amount of disk
space required, the fitted objects are passed through
\code{processFit}. \code{processFit} is a S3 generic function, so it
should be easy to add additional implementations for packages that
are not supported by \pkg{robustlmm} already.

Except for debugging, it is usually not needed to call one of the
\code{fitDatasets} and \code{processFit} functions directly. This is done
either by \code{processDatasetsInParallel} or \code{processFile}.

For smaller simulation studies that can be run on a single machine,
as the ones presented in this vignette, the function
\code{processDatasetsInParallel} does all the work. The datasets are
split into chunks and then run in parallel using \code{parLapply} of
the \pkg{parallel} package \citep{R}.

Larger simulation studies or more computationally expensive methods
may require to be run on a compute server. This is possible, but
requires a bit more work from the user. First, one has to use
\code{saveDatasets} to split the full list of datasets into chunks.
For each of the chunks of datasets, a file is created. Second, these
files need to be distributed to each of the computing machines.
Third, there the files are processed using \code{processFile}.
Fourth and finally, the collected results are merged using
\code{loadAndMergePartialResults}.

To ensure that the results stay valid over a long period of time
(for example when \Rp or packages are updated), one can repeat this
step with \code{checkProcessed} set to true. Then the first dataset
is fit using all the methods and the newly produced results are
compared with the stored results. This is also useful to weed out
hidden dependencies on the random number generator seed.

To satisfy even stricter hard disk space limits (such as when submitting
a package to CRAN), the argument \code{createMinimalSaveFile} can be used.
This creates a file with the processed results of only the first three
generated datasets. This subset of results can be used to run the code
for the next step.

\subsection{Prepare results for plotting}
\label{sec:prepareResultsForPlotting}
The results are stored in a list with several matrices. Using
\code{cbind} these can be converted into a data frame.

If the full results are not permanently stored and only the minimal
results are kept (see last paragraph in previous section), this step will
have to include some code to load the aggreated data from disk and verify
that the data before aggreation hasn't changed. It is good practice to
keep running the aggregation code on the partial results anyway. While
this does not protect against all possible problems, this at least will
make sure that the aggregation code is valid code and can be run.

\subsection{Plotting the results}
\label{sec:plottingTheResults}
This step is entirely up to the user. This vignette uses \pkg{ggplot2}
\citep{ggplot2}, but this is just the author's preference.

\section{List of Functions From Other Packages}
\label{sec:packages}
Several \Rp packages were used to run the simulation studies
presented here. Many functions from \Rp{}'s base packages \citep{R}
were used -- they are not listed here.

Models were fit using code from
\pkg{robustlmm} \citet{robustlmm},
\pkg{lme4} \citep{lme4},
\pkg{robustvarComp} \citep{robustvarComp},
\pkg{heavy} (no longer on  CRAN) \citep{heavy},
\pkg{lqmm} \citep{lqmm}.
Support for \code{rlme} from \pkg{rlme} (no longer on CRAN) \citep{rlme} is also
available, but it was not included in any simulation studies
as all studied datasets were balanced.

The function \code{hubers} from \pkg{MASS}
\citep{MASS} is used to compute robust mean and scale estimates when
aggregating simulation results. The function \code{geom_pointpath} from
\pkg{ggh4x} \citep{ggh4x} is used to draw lines of type \code{b}. Colors
are taken from palettes provided by \pkg{RColorBrewer}
\citep{RColorBrewer}. The skewed t-distribution is implemented in
\pkg{skewt} \citep{skewt}. We ensure file names are file system
compatible using \code{path_sanitize} from \pkg{fs} \citep{fs}.
The function \code{arrange} from \pkg{dplyr} \citep{dplyr} is used to
work around a bug in \pkg{robustvarComp}. The function \code{melt} from
\pkg{reshape2} \citep{reshape2} is used to convert \code{data.frame}
from long to wide format. The function \code{facet_grid2} from
\pkg{ggh4x} \citep{ggh4x} is used to create faceted plots in
grid format that all have different y axes.

\section{Sensitivity Curves}
\label{sec:sensitivityCurves}
This section reproduces the Figure~4.1 in \citet{koller2013diss}
and Figure~1 in \citet{koller2022rse}. The plots have been
defined in \script{sensitivityCurves.R}. Method
\emph{lme} corresponds to \code{fitDataset_lmer},
\emph{RSEn} to \code{fitDatasets_rlmer_DAStau_noAdj} and
\emph{RSEa} to \code{fitDatasets_rlmer_DAStau}.

<<source-sensitivity-curves-code>>=
source(system.file("simulationStudy/sensitivityCurves.R",
                   package = "robustlmm"))
@


<<plot_scLegend,fig=TRUE,include=FALSE,height=1.5,echo=FALSE>>=
print(plot_shiftFirstObservation)
plot_shiftFirstObservation <- plot_shiftFirstObservation +
    theme(legend.position = "none")
plot_shiftFirstGroup <- plot_shiftFirstGroup +
    theme(legend.position = "none")
plot_scaleFirstGroup <- plot_scaleFirstGroup +
    theme(legend.position = "none")
@

<<plot_shiftFirstObservation,fig=TRUE,include=FALSE,height=2>>=
print(plot_shiftFirstObservation)
<<plot_shiftFirstGroup,fig=TRUE,include=FALSE,height=2>>=
print(plot_shiftFirstGroup)
<<plot_scaleFirstGroup,fig=TRUE,include=FALSE,height=2>>=
print(plot_scaleFirstGroup)
@

The plots are shown in Figure~\ref{fig:sensitivityCurvesMixed}.

\begin{figure}[htb]
  \centering
  \caption{Sensitivity curves for a balanced one-way dataset with $10$
    groups of $5$ observations. \emph{lme} is the classical estimator,
    \emph{RSEn} is the RSE estimator for the smoothed Huber
    $\psi$-function where the tuning parameter $k$ for
    \code{rho.sigma.e} is the same as for \code{rho.e} ($k = 1.345$),
    similar for \code{rho.sigma.b} and \code{rho.b}.
    \emph{RSEa} is the same as RSEn, but the
    tuning parameter for \code{rho.sigma.e} and \code{rho.sigma.b}
    are both adjusted ($k = 2.28$).
    Plots from top to bottom correspond to
    \code{plot\_shiftFirstObservation},
    \code{plot\_shiftFirstGroup}, and
    \code{plot\_scaleFirstGroup}.}
  \includegraphics[trim=0 55 0 20,clip,center]
    {simulationStudies-plot_scLegend}
  \includegraphics[center]{simulationStudies-plot_shiftFirstObservation}
  \includegraphics[center]{simulationStudies-plot_shiftFirstGroup}
  \includegraphics[center]{simulationStudies-plot_scaleFirstGroup}
  \label{fig:sensitivityCurvesMixed}
\end{figure}

\section{Consistency and Efficiency}
\label{sec:consistencyAndEfficiency}
\subsection{Diagonal Case}
This section reproduces Figure~4.2, Figure~4.3 and Figure~4.4
in \citet{koller2013diss}. The plots have been
defined in \script{consistencyAndEfficiencyDiagonal.R}.
Method
\emph{lme} corresponds to \code{fitDataset_lmer},
\emph{RSEn} to \code{fitDatasets_rlmer_DAStau_noAdj} and
\emph{RSEa} to \code{fitDatasets_rlmer_DAStau}.
The tuning parameters are set as shown in
Table~\ref{tab:tuningParametersDiagonal}.

\begin{table}[ht]
\centering
\caption{Tuning parameters $k$ used for the smoothed $\psi$-function,
    $s$ is always set to $10$, diagonal case.}
\begingroup\normalsize
\begin{tabular}{r|llll}
    \code{rho.e}              & $0.5$  & $1.345$ & $2$    & $5$     \\
    \hline
    \code{rho.sigma.e} (RSEn) & $0.5$  & $1.345$ & $2$    & $5$     \\
    \code{rho.sigma.e} (RSEa) & $1.47$ & $2.18$  & $2.9$  & $5.03$  \\
    \code{rho.b}              & $0.5$  & $1.345$ & $2$    & $5$ \\
    \code{rho.sigma.b}        & $1.47$ & $2.18$  & $2.9$  & $5.03$
\end{tabular}
\endgroup
\label{tab:tuningParametersDiagonal}
\end{table}

<<source-consistency-and-efficiency-diagonal-code>>=
source(system.file("simulationStudy/consistencyAndEfficiencyDiagonal.R",
                   package = "robustlmm"))
@

<<plot_consistencyDiagonal,fig=TRUE,include=FALSE,height=3>>=
print(plot_consistencyDiagonal)
<<plot_efficiencyDiagonal,fig=TRUE,include=FALSE,height=2.5>>=
print(plot_efficiencyDiagonal)
@

The plots are shown in Figure~\ref{fig:consistencyDiagonal} and
Figure~\ref{fig:efficiencyDiagonal}.

\begin{figure}[htb]
  \centering
  \caption{Mean values and quartiles of $1000$ fits of randomly
    generated, balanced one-way designs with $20$ groups and $20$
    observations per group.
    The tuning parameters for \code{rho.e} are shown on the horizontal
    axis, the corresponding tuning parameters for the other
    functions are shown in Table~\ref{tab:tuningParametersDiagonal}.
    The black line indicates the true values.
    The yellow line shows the classical fit
    (solid: mean, dashed: quartiles).
    Plot corresponds to \code{plot\_consistencyDiagonal}.}
  \includegraphics[center]{simulationStudies-plot_consistencyDiagonal}
  \label{fig:consistencyDiagonal}
  \bigskip
  \caption{For the same setup as shown in
    Figure~\ref{fig:consistencyDiagonal}, the empirical efficiencies
    were computed. The efficiency was computed on the bias corrected
    estimates. The parameters are shown in facet columns, while the rows
    correspond to the same choice of $\psi$-functions, both are
    indicated in the facet strip.
    Plot corresponds to \code{plot\_efficiencyDiagonal}.}
  \includegraphics[center]{simulationStudies-plot_efficiencyDiagonal}
  \label{fig:efficiencyDiagonal}
\end{figure}

\subsection{Block Diagonal Case}
\label{sec:consistencyAndEfficiencyBlockDiagonal}
This section contains a previously unpublished simulation study.
The study is the analogue of study presented in the previous
section, but for a model with a block diagonal random effects
covariance matrix. The study is based upon the Sleep Study dataset
that is also used in Section~\ref{sec:robustnessBlockDiagonal}.
The Sleep Study dataset is available in \pkg{lme4} \citep{lme4}
and was originally published by \citet{belenky03sleepstudy}.

The tuning parameters for the smoothed Huber $\psi$-function
used in this study are as indicated in the charts for \code{rho.e}.
For \code{rho.sigma.e}, the squared robustness weights are used
(\code{psi2propII}). For \code{rho.sigma.b} the same function
is used as for \code{rho.b}. The tuning parameters are set as
shown in Table~\ref{tab:tuningParametersBlockDiagonal}.

\begin{table}[ht]
\centering
\caption{Tuning parameters $k$ used for the smoothed $\psi$-function,
    $s$ is always set to $10$, block diagonal case.}
\begingroup\normalsize
\begin{tabular}{r|llll}
    \code{rho.e}              & $0.5$  & $1.345$ & $2$    & $5$     \\
    \hline
    \code{rho.sigma.e} (RSEn) & $0.5$  & $1.345$ & $2$    & $5$     \\
    \code{rho.sigma.e} (RSEa) & $1.47$ & $2.18$  & $2.9$  & $5.03$  \\
    \code{rho.b}              & $2.17$ & $5.14$  & $8.44$ & $34.21$ \\
    \code{rho.sigma.b}        & $2.17$ & $5.14$  & $8.44$ & $34.21$
\end{tabular}
\endgroup
\label{tab:tuningParametersBlockDiagonal}
\end{table}

<<source-consistency-and-efficiency-block-diagonal-code>>=
source(system.file("simulationStudy/consistencyAndEfficiencyBlockDiagonal.R",
                   package = "robustlmm"))
@

<<plot_consistencyBlockDiagonal,fig=TRUE,include=FALSE,height=3>>=
print(plot_consistencyBlockDiagonal)
<<plot_efficiencyBlockDiagonal,fig=TRUE,include=FALSE,height=2.5>>=
print(plot_efficiencyBlockDiagonal)
@

The plots are shown in Figure~\ref{fig:consistencyBlockDiagonal} and
Figure~\ref{fig:efficiencyBlockDiagonal}. The plots have been
defined in\\ \script{consistencyAndEfficiencyBlockDiagonal.R}.

\begin{figure}[htbp]
  \centering
  \caption{Mean values and quartiles of $1000$ fits of parametric
    bootstrap samples of the Sleep Study dataset.
    The tuning parameters for \code{rho.e} are shown on the horizontal
    axis, the corresponding tuning parameters for the other
    functions are shown in
    Table~\ref{tab:tuningParametersBlockDiagonal}.
    The black line indicates the true values.
    The yellow line shows the classical fit
    (solid: mean, dashed: quartiles).
    Plot corresponds to \code{plot\_consistencyBlockDiagonal}.}
  \includegraphics[center]{simulationStudies-plot_consistencyBlockDiagonal}
  \label{fig:consistencyBlockDiagonal}
  \bigskip
  \caption{For the same setup as shown in
    Figure~\ref{fig:consistencyBlockDiagonal}, the empirical
    efficiencies were computed.
    The efficiency was computed on the bias corrected
    estimates. The parameters are shown in facet columns, while the rows
    correspond to the same choice of $\psi$-functions, both are
    indicated in the facet strip.
    Plot corresponds to \code{plot\_efficiencyBlockDiagonal}.}
  \includegraphics[center]{simulationStudies-plot_efficiencyBlockDiagonal}
  \label{fig:efficiencyBlockDiagonal}
\end{figure}

\subsubsection{Discussion}
The results are similar to the diagonal case presented in the previous
section. It is crucial though that the tuning parameters for \code{rho.b}
and \code{rho.sigma.b} are increased, otherwise the methods are not
consistent (as for $k=0.5$ where even the larger tuning parameters are not
sufficient). This is because squared Mahalanobis distances are used for
blocks of dimension 2 and larger and not simple residuals or predicted
random effects as in the one dimensional case. The expected values of the
distances grows by the dimension of a block, so one has to adjust tuning
parameters per block size.

\section{Breakdown}
\label{sec:breakdown}
This section reproduces Figure~4.5 in \citet{koller2013diss}.
The plots have been defined in \script{breakdown.R}.
Method
\emph{lme} corresponds to \code{fitDataset_lmer},
\emph{RSEn} to\\ \code{fitDatasets_rlmer_DAStau_noAdj} and
\emph{RSEa} to \code{fitDatasets_rlmer_DAStau}.

<<source-breakdown-code>>=
source(system.file("simulationStudy/breakdown.R",
                   package = "robustlmm"))
@

<<plot_breakdown,fig=TRUE,include=FALSE,height=3>>=
print(plot_breakdown)
@

The plot is shown in Figure~\ref{fig:breakdown}.

\begin{figure}[htb]
  \centering
  \caption{Example of breakdown for a one-way random model with
    $20$ groups and $5$ observations per group. Group after group,
    one observation after another was replaced by its absolute
    value multiplied by $10^6$.
    \emph{lme} is the classical estimator,
    \emph{RSEn} is the RSE estimator for the smoothed Huber
    $\psi$-function where the tuning parameter $k$ for
    \code{rho.sigma.e} is the same as for \code{rho.e} ($k = 1.345$),
    similar for \code{rho.sigma.b} and \code{rho.b}.
    \emph{RSEa} is the same as RSEn, but the
    tuning parameter for \code{rho.sigma.e} and \code{rho.sigma.b}
    are both adjusted ($k = 2.28$).
    Plot corresponds to \code{plot\_breakdown}.}
  \includegraphics[center]{simulationStudies-plot_breakdown}
  \label{fig:breakdown}
\end{figure}

\section{Monte-Carlo Breakdown Sweep}
\label{sec:breakdownMC}

This section presents a Monte-Carlo extension of the breakdown
study from Section~\ref{sec:breakdown}. While \script{breakdown.R}
shows a single trajectory under extreme observation-level
contamination ($|y_i| \cdot 10^6$), the study here repeats the
breakdown experiment many times under several distinct
\emph{contamination strategies} and compares the additional fitting
methods provided by the wrappers
\code{fitDatasets_rlmer_DAStau_bisq},
\code{fitDatasets_rlmer_DAStau_sizeOBR},
\code{fitDatasets_rlmer_ransac} and
\code{fitDatasets_rlmer_ransac_bisq} against the classical and
default robust baselines.

\subsection{Setup}

Three default \pkg{robustlmm} designs are used:
\begin{itemize}
    \item \texttt{Dyestuff} (one-way, $6$ batches $\times 5$ replicates)
    \item \texttt{Penicillin} (crossed, $24$ plates $\times 6$ samples)
    \item \texttt{sleepstudy} (block-diagonal, $18$ subjects $\times 10$
        days, with random intercept and slope)
\end{itemize}

For each design, several contamination strategies that differ in
\emph{which} observations or groups are corrupted are tested
(Table~\ref{tab:strategies}). The contamination intensity
$\epsilon \in \{0, 0.05, 0.10, 0.15, 0.20, 0.30, 0.40, 0.50\}$ is
the fraction of observations (or, for RE-level strategies, the
fraction of groups) that are contaminated. The contamination
shifts the corresponding $y$ values by $10 \cdot \sigma_e$ (where
$\sigma_e$ is the true residual scale, taken from the canonical
\code{lmer} fit on the uncontaminated data).

For each (design, strategy, method, $\epsilon$) combination, $30$
random datasets are generated by parametric bootstrap from the
canonical \code{lmer} fit, contaminated, and re-fit. A fit is
``broken'' on indicator $A$ if any of the following indicator
components hold:
\begin{itemize}
    \item \emph{beta}: $|\hat\beta_j - \beta_j| > 5 \cdot
        \mathrm{SE}_{\text{lmer}}(\hat\beta_j)$ for some $j$
    \item \emph{sigma\_e}: $\hat\sigma_e / \sigma_e \notin [1/5, 5]$
    \item \emph{sigma\_b}: $\hat\sigma_{b,k} / \sigma_{b,k} \notin
        [1/5, 5]$ for some $k$
    \item \emph{rho} (sleepstudy only): $|\hat\rho| > 0.99$
\end{itemize}
``Any indicator'' is the OR of those four. The breakdown rate is
the fraction of replicates for which the indicator is true.

\begin{table}[ht]
\centering
\caption{Contamination strategies per design.}
\begin{tabular}{l l p{8cm}}
\toprule
Design & Strategy & Description \\
\midrule
Dyestuff   & \texttt{within}                 & Saturate batch 1, then 2, \ldots \\
           & \texttt{round\_robin}           & One obs per batch in turn \\
           & \texttt{random\_obs}            & Uniform random obs \\
           & \texttt{re\_shift}              & Shift all obs of $\lceil\epsilon \cdot 6\rceil$ batches \\
Penicillin & \texttt{within\_plate}, \texttt{within\_sample}             & Within-plate (resp. sample) saturation \\
           & \texttt{round\_robin\_plate}, \texttt{round\_robin\_sample} & Round-robin across plates (resp. samples) \\
           & \texttt{random\_obs}            & Uniform random obs \\
           & \texttt{re\_shift\_plate}, \texttt{re\_shift\_sample}       & RE-level shift on plate (resp. sample) intercepts \\
sleepstudy & \texttt{within\_subject}        & Saturate subject 1, then 2, \ldots \\
           & \texttt{round\_robin}           & One obs per subject in turn \\
           & \texttt{high\_leverage}         & Highest-\code{Days} obs first \\
           & \texttt{random\_obs}            & Uniform random obs \\
           & \texttt{re\_shift}              & Shift all obs of $\lceil\epsilon \cdot 18\rceil$ subjects \\
           & \texttt{re\_shift\_slope}       & Subject slope shift, centered on \code{Days} \\
\bottomrule
\end{tabular}
\label{tab:strategies}
\end{table}

\subsection{Code}

<<source-breakdownMC-code>>=
source(system.file("simulationStudy/breakdownMC.R",
                   package = "robustlmm"))
@

<<plot_breakdownMC_dyestuff,fig=TRUE,include=FALSE,height=4>>=
print(plot_breakdownMC_dyestuff)
@
<<plot_breakdownMC_penicillin,fig=TRUE,include=FALSE,height=5>>=
print(plot_breakdownMC_penicillin)
@
<<plot_breakdownMC_sleepstudy,fig=TRUE,include=FALSE,height=5>>=
print(plot_breakdownMC_sleepstudy)
@

\begin{figure}[htb]
  \centering
  \caption{Breakdown rate (any indicator) for Dyestuff across the four
    contamination strategies, as a function of $\epsilon$ (fraction of
    contaminated observations or groups). $30$ replicates per cell.}
  \includegraphics[center]{simulationStudies-plot_breakdownMC_dyestuff}
  \label{fig:breakdownMC_dyestuff}
\end{figure}

\begin{figure}[htb]
  \centering
  \caption{Breakdown rate for Penicillin (crossed random effects).}
  \includegraphics[center]{simulationStudies-plot_breakdownMC_penicillin}
  \label{fig:breakdownMC_penicillin}
\end{figure}

\begin{figure}[htb]
  \centering
  \caption{Breakdown rate for sleepstudy (block-diagonal random
    effects with correlation $\rho$).}
  \includegraphics[center]{simulationStudies-plot_breakdownMC_sleepstudy}
  \label{fig:breakdownMC_sleepstudy}
\end{figure}

\section{Convergence}
\label{sec:convergence}
This section contains the details on a simulation study we ran to answer a referee's comment about the convergence of the estimator for increasing sample sizes. The plots have been defined in \script{convergence.R}.
Method
\emph{lme} corresponds to \code{fitDataset_lmer},
\emph{RSEn} to \code{fitDatasets_rlmer_DAStau_noAdj} and
\emph{RSEa} to \code{fitDatasets_rlmer_DAStau}.

\subsection{Setup}
We simulated a balanced dataset with varying number of subjects and replicates per subject. For both, we varied the numbers between 5, 10, 20 and 50. We simulated all 16 combinations. The model we used is
\[
	Y_{hi} = \beta_0 + \beta_{\text{continuous}} x_{\text{continuous}} +
		\beta_{\text{binary}} x_{\text{binary}} + B_h + \varepsilon_{hi}
\]
where $x_{\text{continuous}}$ is a continuous variable generated by a uniform distribution between 0 and 1, $x_{\text{binary}}$ is a binary variable that takes 0 and 1 with equal probability, $\varepsilon_{hi} \sim \mathcal{N}(0, \sigma^2)$ and $B_h \sim \mathcal{N}(0, \sigma_b^2)$, $h = 1, \dots, \text{nSubject}$ and $i = 1, \dots, \text{nReplicates}$.

The other simulation settings are chosen as described in sections 1.4 and 1.4.2 of the manuscript. We show summary statistics (location and scale) computed by using Huber's Proposal 2 using \texttt{hubers} as provided by the \texttt{MASS} \Rp package \citep{MASS}. We also compute the empirical efficiencies by dividing the scale of the classical estimator by the scale of the robust estimator.

<<source-convergence-code>>=
source(system.file("simulationStudy/convergence.R",
                   package = "robustlmm"))
@

<<plot_convergence_N_N_bias,fig=TRUE,include=FALSE,height=6>>=
print(plot_convergence_N_N_bias)
<<plot_convergence_N_N_scale,fig=TRUE,include=FALSE,height=6>>=
print(plot_convergence_N_N_scale)
<<plot_convergence_N_N_efficiency,fig=TRUE,include=FALSE,height=6>>=
print(plot_convergence_N_N_efficiency)
@

\begin{figure}[htb]
  \centering
  \caption{Convergence simulation study, N/N case, bias, estimated
    as the robust mean computed using Huber’s Proposal 2 from
    $1000$ fits.
    \emph{lme} is the classical estimator,
    \emph{RSEn} is the RSE estimator for the smoothed Huber
    $\psi$-function where the tuning parameter $k$ for
    \code{rho.sigma.e} is the same as for \code{rho.e} ($k = 1.345$),
    similar for \code{rho.sigma.b} and \code{rho.b}.
    \emph{RSEa} is the same as RSEn, but the
    tuning parameter for \code{rho.sigma.e} and \code{rho.sigma.b}
    are both adjusted ($k = 2.28$).
    Plot corresponds to \code{plot\_convergence\_N\_N\_bias}.}
  \includegraphics[center]{simulationStudies-plot_convergence_N_N_bias}
  \label{fig:convergenceNnBias}
\end{figure}

\begin{figure}[htb]
  \centering
  \caption{For the same setup as shown in
    Figure~\ref{fig:convergenceNnBias}, but showing the robust scale.
    Plot corresponds to \code{plot\_convergence\_N\_N\_scale}.}
  \includegraphics[center]{simulationStudies-plot_convergence_N_N_scale}
  \label{fig:convergenceNnScale}
\end{figure}

\begin{figure}[htb]
  \centering
  \caption{For the same setup as shown in
    Figure~\ref{fig:convergenceNnBias}, but showing the empirical
    efficiency, computed by dividing the scale of the classical
    estimator by the one of the robust estimator.
    Plot corresponds to \code{plot\_convergence\_N\_N\_efficiency}.}
  \includegraphics[center]{simulationStudies-plot_convergence_N_N_efficiency}
  \label{fig:convergenceNnEfficiency}
\end{figure}

<<plot_convergence_t3_t3_bias,fig=TRUE,include=FALSE,height=6>>=
print(plot_convergence_t3_t3_bias)
<<plot_convergence_t3_t3_scale,fig=TRUE,include=FALSE,height=6>>=
print(plot_convergence_t3_t3_scale)
<<plot_convergence_t3_t3_efficiency,fig=TRUE,include=FALSE,height=6>>=
print(plot_convergence_t3_t3_efficiency)
@

\begin{figure}[htb]
  \centering
  \caption{Convergence simulation study, t3/t3 case, bias, estimated
    as the robust mean computed using Huber’s Proposal 2 from
    $1000$ fits.
    \emph{lme} is the classical estimator,
    \emph{RSEn} is the RSE estimator for the smoothed Huber
    $\psi$-function where the tuning parameter $k$ for
    \code{rho.sigma.e} is the same as for \code{rho.e} ($k = 1.345$),
    similar for \code{rho.sigma.b} and \code{rho.b}.
    \emph{RSEa} is the same as RSEn, but the
    tuning parameter for \code{rho.sigma.e} and \code{rho.sigma.b}
    are both adjusted ($k = 2.28$).
    Plot corresponds to \code{plot\_convergence\_t3\_t3\_bias}.}
  \includegraphics[center]{simulationStudies-plot_convergence_t3_t3_bias}
  \label{fig:convergencet3t3Bias}
\end{figure}

\begin{figure}[htb]
  \centering
  \caption{For the same setup as shown in
    Figure~\ref{fig:convergencet3t3Bias}, but showing the robust scale.
    Plot corresponds to \code{plot\_convergence\_t3\_t3\_scale}.}
  \includegraphics[center]{simulationStudies-plot_convergence_t3_t3_scale}
  \label{fig:convergencet3t3Scale}
\end{figure}

\begin{figure}[htb]
  \centering
  \caption{For the same setup as shown in
    Figure~\ref{fig:convergencet3t3Bias}, but showing the empirical
    efficiency, computed by dividing the scale of the classical
    estimator by the one of the robust estimator.
    Plot corresponds to \code{plot\_convergence\_t3\_t3\_efficiency}.}
  \includegraphics[center]{simulationStudies-plot_convergence_t3_t3_efficiency}
  \label{fig:convergencet3t3Efficiency}
\end{figure}

\subsection{Discussion}
The simulation results are shown in Figure~\ref{fig:convergenceNnBias},
Figure~\ref{fig:convergenceNnScale} and
Figure~\ref{fig:convergenceNnEfficiency} for the N/N case and
Figure~\ref{fig:convergencet3t3Bias},
Figure~\ref{fig:convergencet3t3Scale}
and Figure~\ref{fig:convergencet3t3Efficiency} for the t3/t3 case.

Except for small differences in the case with just 5 subjects, both the classic estimator and our proposed method in the manuscript behave in the same way. Our method is tuned for $95\%$ asymptotic efficiency at the central (N/N) model. The tuning works as expected and is independent of the number of subjects or replicates within subjects, as can be seen in the Figure~\ref{fig:convergenceNnScale}. The empirical efficiency shown in Figure~\ref{fig:convergenceNnEfficiency} reiterates this point. The RSEa method has been tuned for $95\%$ asymptotic efficiency, and this efficiency is surpassed most of the time. Only in the 5 subjects / 5 replicates case the empirical efficiency drops to $80\%$. The empirical efficiency of RSEn is lower, as it is tuned for higher robustness for the scale estimates.

The charts for the t3/t3 case shown in Figure~\ref{fig:convergencet3t3Bias}, Figure~\ref{fig:convergencet3t3Scale} and Figure~\ref{fig:convergencet3t3Efficiency} also show expected behavior. Neither method shows a large bias for the betas as the contamination is symmetric. Our method shows less bias for $\hat\sigma$ and $\hat\sigma_b$ as expected for a robust method. The empirical efficiency is higher for our method throughout, except for one the 5 subjects / 5 replicates case where the empirical efficiency for $\hat\sigma_b$ comes out slightly lower. The higher robustness for the scale estimates pays off with a higher empirical efficiency for RSEn than RSEa.

\section{Robustness and Empirical Test Coverage}
\label{sec:robustnessAndCoverage}
\subsection{Diagonal Case}
This section replicates the simulation study in Section~4.2 in
\citet{koller2022rse} (Figure~2 and Figure~3). The plots have been
defined in \script{robustnessDiagonal.R}. Method
\emph{lme} corresponds to \code{fitDataset_lmer},
\emph{RSEn} to \code{fitDatasets_rlmer_DAStau_noAdj} and
\emph{RSEa} to \code{fitDatasets_rlmer_DAStau}.

<<source-robustness-diagonal-code>>=
source(system.file("simulationStudy/robustnessDiagonal.R",
                   package = "robustlmm"))
@

<<plot_robustnessDiagonal,fig=TRUE,include=FALSE,height=7.5>>=
print(plot_robustnessDiagonal)
<<plot_coverageDiagonal,fig=TRUE,include=FALSE,height=2.5>>=
print(plot_coverageDiagonal)
@

The plots are shown in Figure~\ref{fig:robustnessDiagonal},
Figure~\ref{fig:coverageDiagonal}.

\begin{figure}[htb]
  \centering
  \caption{Simulation results for the diagonal case. The left column
    shows a robust location estimate of the simulated estimates for the
    diverse methods and the five parameters
    $\beta_0, \beta_1, \beta_2, \sigma, \sigma_b$. The true values are
    indicated by gray horizontal lines.
    Deviations from them thus represent biases.
    The right column shows robust scale parameters---measures of
    simulated standard errors---in an analogous way.
    Plot corresponds to \code{plot\_robustnessDiagonal}.}
  \includegraphics[center]{simulationStudies-plot_robustnessDiagonal}
  \label{fig:robustnessDiagonal}
\end{figure}

\begin{figure}[htb]
  \centering
  \caption{Simulation results for the diagonal case showing empirical
  	coverage probabilities for the intercept $\beta_0$ (left),
	$\beta_1$ (middle) and $\beta_2$ (right).
	The expected level of $0.95$ is shown by a gray line.
    Plot corresponds to \code{plot\_coverageDiagonal}.}
  \includegraphics[center]{simulationStudies-plot_coverageDiagonal}
  \label{fig:coverageDiagonal}
\end{figure}

\subsection{Block Diagonal Case}
\label{sec:robustnessBlockDiagonal}
This section replicates the simulation study in Section~4.4 in
\citet{koller2022rse} (Figure~4 and Figure~5). The plots have been
defined in \script{robustnessBlockDiagonal.R}.

<<source-robustness-block-diagonal-code>>=
source(system.file("simulationStudy/robustnessBlockDiagonal.R",
                   package = "robustlmm"))
@

<<plot_robustnessBlockDiagonal,fig=TRUE,include=FALSE,height=7.5>>=
print(plot_robustnessBlockDiagonal)
<<plot_violinBlockDiagonal,fig=TRUE,include=FALSE,height=7.5>>=
print(plot_violinBlockDiagonal)
@

The plots are shown in Figure~\ref{fig:robustnessBlockDiagonal},
Figure~\ref{fig:violinBlockDiagonal}.

\begin{figure}[htb]
  \centering
  \caption{Simulation results for the block-diagonal case, shown as in
    Figure~\ref{fig:robustnessDiagonal}.
    Plot corresponds to \code{plot\_robustnessBlockDiagonal}.}
  \includegraphics[center]{simulationStudies-plot_robustnessBlockDiagonal}
  \label{fig:robustnessBlockDiagonal}
\end{figure}

\begin{figure}[htb]
  \centering
  \caption{Simulated distribution of estimates in the block-diagonal
    case. The green horizontal line marks the true value.
    Plot corresponds to \code{plot\_violinBlockDiagonal}.}
  \includegraphics[center]{simulationStudies-plot_violinBlockDiagonal}
  \label{fig:violinBlockDiagonal}
\end{figure}

\clearpage
\section{Studies new in the 3.5.0 release}
\label{sec:new350}

The 3.5.0 release adds a number of new methods (robust inference,
influence diagnostics, a \textsc{ransac} initial estimator, Mallows
design weights and structured random-effect covariances; see
\code{vignette("robustlmm-3.5.0")}). Each is backed by one or more
simulation studies. The scripts are available in the package sources
on GitHub and are linked below; the worked,
\Sexpr{}-reproducible headline numbers appear in the
\code{robustlmm-3.5.0} vignette. This section describes each study's
design and conclusions. Because the full result objects exceed the
CRAN size limit, the figures are not rebuilt here; install the
\code{full-results} version (Section~\ref{sec:thecode}) to regenerate
them.

\subsection{Inference: coverage, size and power}
\label{sec:inferenceStudy}

\script{inferenceStudy.R} evaluates the finite-sample behaviour
of the new inference methods (the cluster-sandwich \code{vcov}, the
Wald and bootstrap \code{confint}, and the robust \code{anova}). It
sweeps the number of groups $J$ and several error- and
random-effect-contamination strategies, and records, for each method:
the empirical coverage of the nominal $95\%$ confidence intervals, the
Type-I error of the tests at nominal $5\%$, and the power under a
fixed-effect alternative.

\emph{Conclusions.} On clean Gaussian data all methods are
well-calibrated. Under error-distribution contamination the robust
Wald intervals are markedly shorter than the classical ones at matched
coverage, and the robust Wald \code{anova} is more powerful than the
classical likelihood-ratio test while holding its size. The
cluster-sandwich covariance is a large-$J$ device: at small $J$ it
under-covers and the sandwich-based tests become anti-conservative,
returning to nominal only for larger $J$; \code{vcov_sandwich} warns
below $20$ clusters, and the conclusions are distilled into the
\code{?vcov\_sandwich} and \code{?anova.rlmerMod} caveats. The
variance-component bootstrap test (\code{anova(f0, f1, test =
  "boot"})) calibrates correctly under error contamination but is
anti-conservative when whole groups are contaminated, which motivates
the cluster-level influence diagnostics below.

\subsection{Leverage breakdown and gross-error sensitivity}
\label{sec:leverageBreakdown}

The $\psi$-functions of the RSE bound the influence of outliers in the
response but not of high-leverage design points.
\script{leverageBreakdown.R} contaminates a fraction of the
observations by moving them onto a spurious regression line at a large
robust distance in covariate space, and compares the slope bias of
\code{lmer}, the plain RSE and the Mallows-weighted RSE
(\code{design.weights = "mcd"}) as the contamination fraction grows.
\script{leverageGES.R} complements it by tracing the
gross-error sensitivity --- the supremum of the (standardised)
influence as a single design point is moved out along a covariate.

\emph{Conclusions.} The plain RSE breaks down with \code{lmer} under
leverage contamination (good-leverage outliers have small residuals,
so the $\psi$-functions do not down-weight them), whereas the
Mallows-weighted fit stays close to the clean-data estimate, with
several-fold smaller slope bias at moderate contamination. The
gross-error sensitivity of the plain RSE is unbounded in the leverage
direction, while the Mallows weights make it bounded.

\subsection{Efficiency cost of the Mallows weights}
\label{sec:mallowsEfficiency}

\script{mallowsEfficiency.R} quantifies the price of the
leverage protection at the clean Gaussian model across the tuning grid
($\gamma \in \{1, 2\}$, cut-off $\in \{0.95, 0.975, 0.99\}$), reporting
the relative efficiency of $\hat\beta$ against \code{lmer} (the MLE)
and against the plain RSE. \script{mallowsEfficiencyDim.R}
repeats this as the number of continuous covariates grows, to check
that the $\chi^2$-calibrated cut-off keeps the false-down-weighting
rate roughly constant with dimension.

\emph{Conclusions.} At the default tuning ($\gamma = 1$, cut-off
$0.975$) the clean-model cost is about $\Sexpr{fmt(nums$mallows$costVsRsePct, 1)}\%$
in the variance of $\hat\beta$ relative to the plain RSE (over
$\Sexpr{nums$mallows$nreps}$ replicates), with only about
$\Sexpr{fmt(nums$mallows$pctDown, 1)}\%$ of clean observations
down-weighted; the cost is stable as the covariate dimension grows and
vanishes for designs without continuous covariates. Only the most
aggressive corner of the grid ($\gamma = 2$, cut-off $0.95$) costs
appreciably more, which is why the gentler default is shipped.

\subsection{RANSAC: basin radius and multi-start consensus}
\label{sec:ransacStudies}

\script{ransacBasin.R} separates the two failure modes the
\code{rlmer_ransac} diagnostics watch for: a \emph{fixed-effect basin}
condition (a redescending \code{rho.e} only keeps the high-breakdown
\textsc{ransac} start if the start lies within the bisquare
support-preservation radius $r^\ast$) and a distinct
\emph{random-effects} attractor in which the fitted RE correlation
collapses to a phony $|\hat\rho| \to 1$. \script{ransacConsensus.R}
then evaluates the multi-start consensus (\code{rlmer_ransac(n_starts =
  m)}) under several error/RE contamination generators.

\emph{Conclusions.} The basin radius is genuinely a fixed-effect
property and the phony-correlation failure is a separate random-effects
phenomenon, so the two are reported by separate diagnostics. Running
\code{rlmer} from the several best distinct \textsc{ransac} starts and
keeping the interior, lowest-residual-scale fit markedly reduces the
rate of phony $|\hat\rho| \to 1$ solutions relative to a single start,
at essentially unchanged slope accuracy --- recovering the good
solution when the single best start falls into the phony attractor.

\subsection{Structured random-effect covariances}
\label{sec:structuredCovariances}

\script{structuredCovariances.R} studies the new \code{cs} and
\code{ar1} covariance structures. Balanced repeated-measures datasets
(generated with \code{generateRepeatedMeasuresDatasets}) are fitted
classically (\code{lmer}) and robustly (\code{rlmer}) under both clean
data and random-effects-level contamination, and the recovered common
correlation and marginal standard deviations are compared with the
truth.

\emph{Conclusions.} On clean data both fits recover the truth, so the
projection-based fitting is consistent. Under contamination of
$\Sexpr{nums$structured$contamPct}\%$ of the subjects the classical
common correlation collapses toward zero (from a true
$\Sexpr{fmt(nums$structured$cs$trueRho)}$ to
$\Sexpr{fmt(nums$structured$cs$contLmer)}$ for \code{cs} and from
$\Sexpr{fmt(nums$structured$ar1$trueRho)}$ to
$\Sexpr{fmt(nums$structured$ar1$contLmer)}$ for \code{ar1}) and the
marginal standard deviations inflate, whereas the robust structured fit
stays near the truth (correlation
$\Sexpr{fmt(nums$structured$cs$contRlmer)}$ and
$\Sexpr{fmt(nums$structured$ar1$contRlmer)}$). The \code{cs}/\code{ar1}
fitting is experimental (see \code{vignette("robustlmm-3.5.0")}).

\clearpage
\section{Session Info}
\label{sec:sessionInfo}
<<sessionInfo,results=tex,echo=FALSE>>=
sub("robustlmm~03.0", "robustlmm~3.0", sub("/Resources", "/|\n\\\\verb|Resources", attr(results, "sessionInfo")))
@

\bibliography{simulationStudies}

\end{document}
