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\title{Linear mixed model implementation in lme4}
\author{Douglas Bates\\Department of Statistics\\%
  University of Wisconsin -- Madison}
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\begin{abstract}
  We describe the form of the linear mixed-effects and generalized
  linear mixed-effects models fit by \code{lmer} and give details of
  the representation and the computational techniques used to fit such
  models.  These techniques are illustrated on several examples.
\end{abstract}

<<preliminaries,echo=FALSE,print=FALSE>>=
options(width=80, show.signif.stars = FALSE,
        lattice.theme = function() canonical.theme("pdf", color = FALSE))
library(lattice)
library(Matrix)
library(lme4)
data("Rail", package = "nlme")
data("ScotsSec", package = "mlmRev")
Rail <- data.frame(travel = Rail$travel, Rail = Rail$Rail)
@

\section{A simple example}
\label{sec:example}

The \code{Rail} data set from the \package{nlme} package is described
in \citet{pinh:bate:2000} as consisting of three measurements of
the travel time of a type of sound wave on each of six sample railroad
rails. We can examine the structure of these data with the \code{str}
function
<<strRail>>=
str(Rail)
@

Because there are only three observations on each of the rails a
dotplot (Figure~\ref{fig:Raildotplot}) shows the structure of the data
well.
<<Raildotplot,fig=TRUE,include=FALSE,width=8,height=2>>=
print(dotplot(Rail~travel,Rail,xlab="Travel time (ms)",ylab="Rail number"))
@
\begin{figure}[tb]
  \centering
  \includegraphics{Implementation-Raildotplot}
  \caption{Travel time of sound waves in a sample of six railroad
    rails.  There were three measurements of the travel time on each
    rail. The rail numbers are sorted by increasing mean travel time.}
  \label{fig:Raildotplot}
\end{figure}

In building a model for these data
<<Raildata>>=
Rail
@
we wish to characterize a typical travel time, say $\mu$, for the
population of such railroad rails and the deviations, say
$b_i,i=1,\dots,6$ of the individual rails from this population mean.
Because these specific rails are not of interest by themselves as much
as the variation in the population we model the $b_i$, which are
called the ``random effects'' for the rails, as having a normal
(also called ``Gaussian'') distribution of the form $\mathcal{N}(0,\sigma^2_b)$.  The
$j$th measurement on the $i$th rail is expressed as
\begin{equation}
  \label{eq:1}
  y_{ij}=\mu+b_i+\epsilon_{ij}\quad
  b_i\sim\mathcal{N}(0,\sigma^2_b),\epsilon_{ij}\sim\mathcal{N}(0,\sigma^2)
  \quad  i=1,\dots,6\;j=1,\dots,3
\end{equation}

The parameters of this model are $\mu$, $\sigma^2_b$ and $\sigma^2$.
Technically the $b_i,i=1,\dots,6$ are not parameters but instead are
considered to be unobserved random variables for which we form
``predictions'' instead of ``estimates''.

To express generalizations of models like (\ref{eq:1}) more
conveniently we switch to a matrix/vector
representation in which the 18 observations of the travel time form
the response vector $\bm{y}$, the fixed-effect parameter $\mu$ forms a
$1$-dimensional column vector $\bm{\beta}$ and the six random effects
$b_i,i=1,\dots,6$ form the random effects vector $\bm{b}$.  The
structure of the data and the values of any covariates (none are used
in this model) are used to create model matrices $\bm{X}$ and
$\bm{Z}$.

Using these vectors and matrices and the $18$-dimensional vector
$\bm{\epsilon}$ that represents the per-observation noise terms the
model becomes
\begin{equation}
  \label{eq:lmm}
  \bm{y} = \bm{X}\bm{\beta} + \bm{Z}\bm{b}+\bm{\epsilon},\quad
  \bm{\epsilon}\sim\mathcal{N},\left(\bm{0},\sigma^2\bm{I}\right),\;
  \bm{b}\sim\mathcal{N}\left(\bm{0},\sigma^2\bm{\Sigma}\right)\;\mathrm{and}\;
  \bm{b}\perp\bm{\epsilon}
\end{equation}

In the general form we write $p$ for the dimension of $\bm{\beta}$,
the fixed-effects parameter vector, and $q$ for the dimension of
$\bm{b}$, the vector of random effects.  Thus the model matrix
$\bm{X}$ has dimension $n\times p$, the model matrix $\bm{Z}$ has
dimension $n\times q$ and the relative variance-covariance matrix,
$\bm{\Sigma}$, for the random-effects has dimension $q\times q$.  The
symbol $\perp$ indicates independence of random variables and
$\mathcal{N}$ denotes the multivariate normal (Gaussian) distribution.

We say that matrix $\bm{\Sigma}$ is the relative variance-covariance
matrix of the random effects in the sense that it is the variance of
$\bm{b}$ relative to $\sigma^2$, the scalar variance of the
per-observation noise term $\bm{\epsilon}$.  Although it size, $q$,
can be very large, $\bm{\Sigma}$ is highly structured.  It is
symmetric, positive semi-definite and zero except for the diagonal
elements and certain elements close to the diagonal.

\subsection{Fitting the model and examining the results}
\label{sec:fitt-model-exam}

The maximum likelihood estimates for parameters in model (\ref{eq:1})
fit to the \code{Rail} data are obtained as
<<RailfitML>>=
Rm1ML <- lmer2(travel ~ 1 + (1|Rail), Rail, method = "ML",
               control = list(msVerbose = 1))
@
In this fit we have set the control parameter \code{msVerbose} to 1
indicating that information on the progress of the iterations should
be printed after every iteration.  Each line gives the iteration
number, the value of the deviance (negative twice the log-likelihood)
and the value of the parameter $s$ which is the standard deviation of
the random effects relative to the standard deviation of the
residuals.

The printed form of the model
<<Railfitshow>>=
Rm1ML
@
provides additional information about the parameter estimates and some
of the measures of the fit such as the log-likelihood (-64.28), the
deviance for the maximum likelihood criterion (128.6), the deviance
for the REML criterion (122.2), Akaike's Information Criterion
(AIC$=132.6$) and Schwartz's Bayesian Information Criterion
(BIC$=134.3$).

The model matrices $\bm{Z}$ and $\bm{X}$ and the negative of the
response vector $-\bm{y}$ are stored in the \code{ZXyt} slot in the
transposed form.  Extracting the transpose of this slot
<<ZXytRail>>=
t(Rm1ML@ZXyt)
@
The first 6 columns of this matrix are $\bm{Z}$, the seventh column is
$\bm{X}$ and the eighth and final column is $-\bm{y}$.  As indicated
in the display of the matrix, it is stored as a sparse matrix.  The
elements represented as `.' are known to be zero and are not stored
explicitly.

The columns of $\bm{Z}$ are indicator columns (that is, the $i$th column has
a 1 in row $j$ if the $j$th observation is on rail $i$, otherwise it
is zero) but they are not in the usual ordering.  This is because the
levels of the \code{Rail} factor have been reordered according to
increasing mean response for Figure~\ref{fig:Raildotplot}.

The crossproduct of the columns of this matrix are stored as a
symmetric, sparse matrix in the \code{A} slot.
<<ARm1ML>>=
Rm1ML@A
@

The \code{L} component of this fitted model is a Cholesky
factorization of a matrix $\bm{A}^*(\bm{\theta})$ where $\bm{\theta}$ is a
parameter vector determining $\bm{\Sigma}(\bm{\theta})$.  This matrix can be
factored as $\bm{\Sigma}=\bm{T}\bm{S}\bm{S}\bm{T}\trans$, where
$\bm{T}$ is a unit, lower triangular matrix (that is, all the elements
above the diagonal are zero and all the elements on the diagonal are
unity) and $\bm{S}$ is a diagonal matrix with non-negative elements on
the diagonal.
The matrix $\bm{A}^*(\bm{\theta})$ is
\begin{equation}
  \label{eq:Astar}
  \begin{split}
    \bm{A}^*(\bm{\theta})&=
    \begin{bmatrix}
      {\bm{Z}^*}\trans\bm{Z}^*+\bm{I} & {\bm{Z}^*}\trans\bm{X} &
      -{\bm{Z}^*}\trans\bm{y}\\
      {\bm{X}}\trans\bm{Z}^* & {\bm{X}}\trans\bm{X} &
      -{\bm{X}}\trans\bm{y}\\
      -{\bm{y}}\trans\bm{Z}^*&-{\bm{y}}\trans\bm{X}&{\bm{y}}\trans\bm{y}
    \end{bmatrix} \\
    &=
    \begin{bmatrix}
      \bm{T}\trans\bm{S} & \bm{0} & \bm{0} \\
      \bm{0} & \bm{I} & \bm{0} \\
      \bm{0} & \bm{0} & 1
    \end{bmatrix}
    \bm{A}
    \begin{bmatrix}
      \bm{S}\bm{T} & \bm{0} & \bm{0} \\
      \bm{0} & \bm{I} & \bm{0} \\
      \bm{0} & \bm{0} & 1
    \end{bmatrix}+
    \begin{bmatrix}
      \bm{I} & \bm{0} & \bm{0} \\
      \bm{0} & \bm{0} & \bm{0} \\
      \bm{0} & \bm{0} & 0
    \end{bmatrix} .
  \end{split}
\end{equation}

For model (\ref{eq:1}) the matrices $\bm{T}$ and $\bm{S}$ are
particularly simple, $\bm{T}=\bm{I}_6$, the $6\times 6$ identity matrix
and $\bm{S}=s_{1,1}\bm{I}_6$ where $s_{1,1}=\sigma_b/\sigma$ is the
standard deviation of the random effects relative to the standard
deviation of the per-observation noise term $\bm{\bm{\epsilon}}$.

<<fewdigits,echo=FALSE,results=hide>>=
op <- options(digits=4)
@
The Cholesky decomposition of $\bm{A}^*$ is a lower triangular sparse
matrix $\bm{L}$
<<LRm1ML>>=
as(Rm1ML@L, "sparseMatrix")
@
<<unfewdigits,echo=FALSE,results=hide>>=
options(op)
@

As explained in later sections the matrix $\bm{L}$ provides all the
information needed to evaluate the ML deviance or the REML deviance as
a function of $\bm{\theta}$.  The components of the deviance are given
in the \code{deviance} slot of the fitted model
<<devRm1ML>>=
Rm1ML@deviance
@
The element labelled \code{ldZ} is the logarithm of the square of
the determinant of the upper left $6\times 6$ section of $\bm{L}$.
This corresponds to
$\log\left|{\bm{Z}^*}\trans\bm{Z}^*+\bm{I}_q\right|$ where
$\bm{Z}^*=\bm{Z}\bm{T}\bm{S}$.  We can verify that the value
$27.38292$ can indeed be calculated in this way.
<<ldZRm1ML>>=
L <- as(Rm1ML@L, "sparseMatrix")
2*sum(log(diag(L)[1:6]))
@

The \code{lr2} element of the \code{deviance} slot is the logarithm of
the penalized residual sum of squares.  It can be calculated as the
logarithm of the square of the last diagonal element in $\bm{L}$.
<<lr2Rm1ML>>=
2*log(L[8,8])
@

For completeness we mention that the \code{ldX} element of the
\code{deviance} slot is the logarithm of the product of the squares of
the diagonal elements of $\bm{L}$ corresponding to columns of $\bm{X}$.
<<ldXRm1ML>>=
2*log(L[7,7])
@
This element is used in the calculation of the REML criterion.

Another slot in the fitted model object is \code{dims}, which
contains information on the dimensions of the model and some of the
characteristics of the fit.
<<dimsRm1ML>>=
Rm1ML@dims
@
We can reconstruct the ML estimate of the residual variance as the
penalized residual sum of squares divided by the number of
observations.
%% FIXME: This is not calculated correctly in the current version of lmer2
<<s2Rm1ML>>=
exp(Rm1ML@deviance["lr2"])/Rm1ML@dims["n"]
@

The \emph{profiled deviance} function
\begin{equation}
  \label{eq:2}
  \begin{aligned}
  \tilde{\mathcal{D}}(\bm{\theta}) &= \log\left|{\bm{Z}^*}\trans\bm{Z}^*+\bm{I}_q\right| +
  n \log \left(1+\frac{2\pi r^2}{n}\right) \\
  &= n\left[1+\log\left(\frac{2\pi}{n}\right)\right] +
    \log\left|{\bm{Z}^*}\trans\bm{Z}^*+\bm{I}_q\right| + n\log r^2
  \end{aligned}
\end{equation}
is a function of $\bm{\theta}$ only.  In this case
$\bm{\theta}=\sigma_1$, the relative standard deviation of the random
effects, is one-dimensional. The maximum likelihood estimate (mle) of
$\bm{\theta}$ minimizes the profiled deviance. The mle's of all the
other parameters in the model can be derived from the estimate of this
parameters.

<<devcomp,echo=FALSE,fig=TRUE,width=8, height=2.5>>=
mm <- Rm1ML
sg <- seq(0, 20, len = 101)
dev <- mm@deviance
nc <- length(dev)
nms <- names(dev)
vals <- matrix(0, nrow = length(sg), ncol = nc, dimnames = list(NULL, nms))
for (i in seq(along = sg)) {
    .Call("lmer2_setPars", mm, sg[i], PACKAGE = "lme4")
    vals[i,] <- mm@deviance
}
vals[,"lr2"] <- mm@dims["n"] * vals[,"lr2"]
df <- data.frame(x = rep(sg, nc), y = as.vector(vals),
                 type = gl(nc, length(sg), labels = nms))
print(xyplot(y ~ x | type, df, type = c("g", "l"),
             subset = !(type %in% c("REML", "ldX", "bqd", "Sdr")),
             scales = list(x = list(axs = 'i'),
             y = list(relation = "free", rot = 0)),
             aspect = 0.75, xlab = expression(sigma[1]), ylab = '',
             layout = c(3,1)))
@

The term $n\left[1+\log\left(2\pi/n\right)\right]$ in (\ref{eq:2})
does not depend on $\bm{\theta}$.  The other two terms,
$\log\left|{\bm{Z}^*}\trans\bm{Z}^*+\bm{I}_q\right|$ and $n\log r^2$,
measure the complexity of the model and the fidelity of the fitted
values to the observed data, respectively.  We plot the value of each
of the varying terms versus $\sigma_1$ in Figure~\ref{fig:devcomp}.
\begin{figure}[tb]
  \centering
  \includegraphics{Implementation-devcomp}
  \caption{The profiled deviance, and those components of the profiled
    deviance that vary with $\bm{\theta}$, as a function of
    $\bm{\theta}$ in model \code{Rm1ML} for the \code{Rail} data. In
    this model the parameter $\bm{\theta}$ is the scalar $\sigma_1$,
    the standard deviation of the random effects relative to the
    standard deviation of the per-observation noise.}
  \label{fig:devcomp}
\end{figure}

The component $\log\left|\bm{S}\bm{Z}\trans\bm{Z}\bm{S}+\bm{I}\right|$
has the value $0$ at $\sigma_1=0$ and increases as $\sigma_1$
increases.  It is unbounded as $\sigma_1\rightarrow\infty$.  The
component $n\log\left(r^2\right)$ has a finite value at $\sigma_1=0$
from which it decreases as $\sigma_1$ increases.  The value at
$\sigma_1=0$ corresponds to the residual sum of squares for the
regression of $\bm{y}$ on the columns of $\bm{X}$.
<<leftlr2>>=
18 * log(deviance(lm(travel ~ 1, Rail)))
@
As $\sigma_1\rightarrow\infty$, $n\log\left(r^2\right)$ approaches the
value corresponding to the residual sum of squares for the regression
of $\bm{y}$ on the columns of $\bm{X}$ and $\bm{Z}$.  For this model
that is
<<rightlr2>>=
18 * log(deviance(lm(travel ~ Rail, Rail)))
@

<<devcomp2,echo=FALSE,fig=TRUE,height=4>>=
mm <- Rm1ML
sg <- seq(3.75, 8.25, len = 101)
dev <- mm@deviance
nc <- length(dev)
nms <- names(dev)
vals <- matrix(0, nrow = length(sg), ncol = nc, dimnames = list(NULL, nms))
for (i in seq(along = sg)) {
    .Call("lmer2_setPars", mm, sg[i], PACKAGE = "lme4")
    vals[i,] <- mm@deviance
}
base <- 18 * log(deviance(lm(travel ~ Rail, Rail)))
print(xyplot(devC + ldZ ~ sg,
             data.frame(ldZ = vals[,"ldZ"], devC =
                        vals[, "ldZ"] + mm@dims['n'] * vals[, "lr2"] - base,
                        sg = sg), type = c("g", "l"),
             scales = list(x = list(axs = 'i')), aspect = 1.8,
             xlab = expression(sigma[1]), ylab = "Shifted deviance",
             auto.key = list(text = c("deviance", "log|SZ'ZS + I|"),
             space = "right", points = FALSE, lines = TRUE)))
@
\begin{figure}[tb]
  \centering
  \includegraphics[width=0.7\textwidth]{Implementation-devcomp2}
  \caption{The part of the deviance that varies with $\sigma_1$ as a
    function of $\sigma_1$ near the optimum.  The component
    $\log\left|\bm{S}\bm{Z}\trans\bm{Z}\bm{S}+\bm{I}\right|$ is shown
    at the bottom of the frame.  This is the component of the deviance
    that increases with $\sigma_1$.  Added to this component is
    $n\log\left[r^2(\sigma_1)\right] - n\log\left[r^2(\infty)\right]$,
    the comonent of the deviance that decreases as $\sigma_1$
    increases.  Their sum is minimized at $\widehat{\sigma}_1=5.626$.}
  \label{fig:devcomp2}
\end{figure}

\section{Multiple random effects per level}
\label{sec:multiple-re}

<<sleepxyplot,fig=TRUE,echo=FALSE,height=7,width=7>>=
print(xyplot(Reaction ~ Days | Subject, sleepstudy, aspect = "xy",
             layout = c(6,3), type = c("g", "p", "r"),
             index.cond = function(x,y) coef(lm(y ~ x))[1],
             xlab = "Days of sleep deprivation",
             ylab = "Average reaction time (ms)"))
@

The \code{sleepstudy} data are an example of longitudinal data.  That
is, they are repeated measurements taken on the same experimental units
over time.  A plot of reaction time versus days of sleep deprivation
by subject (Figure~\ref{fig:sleepxyplot})
\begin{figure}[tbp]
  \centerline{\includegraphics[width=\textwidth]{Implementation-sleepxyplot}}
  \caption{A lattice plot of the average reaction time versus number
    of days of sleep deprivation by subject for the \code{sleepstudy}
    data.  Each subject's data are shown in a separate panel, along
    with a simple linear regression line fit to the data in that
    panel.  The panels are ordered, from left to right along rows
    starting at the bottom row, by increasing intercept of these
    per-subject linear regression lines.  The subject number is given
    in the strip above the panel.}
  \label{fig:sleepxyplot}
\end{figure}
shows there is considerable variation between subjects in both the
intercept and the slope of the linear trend.

The model
<<sm1>>=
print(sm1 <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy))
@
provides for fixed effects for the intercept (the typical reaction
time in the population after zero days of sleep deprivation) and the
slope with respect to \code{Days} (the typical change in reaction time
per day of sleep deprivation).  In addition there are random effects
per subject for both the intercept and the slope parameters.

<<sm1struct,fig=TRUE,echo=FALSE,height=3,width=6>>=
print(image(sm1@A, colorkey = FALSE, aspect = 1),
      split = c(1,1,2,1), more = TRUE)
print(image(sm1@L, colorkey = FALSE, aspect = 1),
      split = c(2,1,2,1))
@

With two random effects per subject the matrix $\bm{\Sigma}$ for this
model is $36\times 36$ with $18$ $2\times 2$ diagonal blocks.  The
matrix $\bm{A}$ is $39\times 39$ as is $\bm{L}$, the Cholesky factor
of $\bm{A}^*$.  The structure of $\bm{A}$ and of $\bm{L}$ are shown in
Figure~\ref{fig:ALstruct}.
\begin{figure}[tbp]
  \centerline{\includegraphics[width=\textwidth]{Implementation-sm1struct}}
  \caption{Structure of the sparse matrices $\bm{A}$ (left panel) and
    $\bm{L}$ (right panel) for the model \code{sm1}.  The non-zero
    elements as depicted as gray squares with larger magnitudes shown as
    darker gray.}
    \label{fig:ALstruct}
\end{figure}
For this model (as for all models with a
single random effects expression) the structure of $\bm{L}$ is
identical to that of the lower triangle of $\bm{A}$.

Like the \code{Rail} data, the \code{sleepstudy} data are balanced in
that each subject's reaction time is measured the same number of times
and at the same times.  One consequence of the balance is that the
diagonal blocks in the first $36$ rows of $\bm{A}$ are identical as
are those in the first $36$ rows of $\bm{L}$.
<<A1212>>=
sm1@A[1:2,1:2]
sm1@A[7:8,7:8]
as(sm1@L, "sparseMatrix")[1:2,1:2]
as(sm1@L, "sparseMatrix")[7:8,7:8]
@

The determinant quantities in
<<sm1deviance>>=
sm1@deviance
@
are derived from the diagonal elements of $\bm{L}$.  \code{ldZ} is the
logarithm of square of the product of the first $36$ elements of the
diagonal, \code{ldX} is the logarithm of the square of the product of
the $37$th and $38$th elements and \code{lr2} is the logarithm of the
square of the $39$th element.
<<sm1reconstruct>>=
str(dd <- diag(as(sm1@L, "sparseMatrix")))
c(ldZ = sum(log(dd[1:36]^2)), ldX = sum(log(dd[37:38]^2)), lr2 = log(dd[39]^2))
@

The $36\times 36$ matrices $\bm{S}$, $\bm{T}$ and
$\bm{\Sigma}=\bm{T}\bm{S}\bm{S}\bm{T}\trans$ are block-diagonal
consisting of $18$ identical $2\times 2$ diagonal blocks.  The
template for the diagonal blocks of $\bm{S}$ and $\bm{T}$ is stored as
a single matrix
<<sm1ST>>=
show(st <- sm1@ST$Subject)
@ %$
where the diagonal elements are those of $\bm{S}$ and the strict lower
triangle is that of $\bm{T}$.

The \code{VarCorr} generic function extracts the estimates of the
variance-covariance matrices of the random effects. Because model
\code{sm1} has a single random-effects expression there is only one
estimated variance-covariance matrix
<<sm1VarCorr>>=
show(vc <- VarCorr(sm1))
@
The \code{"sc"} attribute of this matrix is the estimate of $\sigma$,
the standard deviation of the per-observation noise term.

We can reconstruct this variance-covariance estimate as
<<sm1VarCorrRec>>=
T <- st
diag(T) <- 1
S <- diag(diag(st))
T
S
T %*% S %*% S %*% t(T) * attr(vc, "sc")^2
@

\section{A model with two nested grouping factors}
\label{sec:nested}


<<Oatsxy,fig=TRUE,echo=FALSE,height=4,width=6>>=
data(Oats, package = 'nlme')
print(xyplot(yield ~ nitro | Block, Oats, groups = Variety, type = c("g", "b"),
             auto.key = list(lines = TRUE, space = 'top', columns = 3),
             xlab = "Nitrogen concentration (cwt/acre)",
             ylab = "Yield (bushels/acre)",
             aspect = 'xy'))
@
The \code{Oats} data from the \code{nlme} package came from an
experiment in which fields in $6$ different locations (the
\code{Block} factor) were each divided into three plots and each of
these $18$ plots was further subdivided into four subplots.  Three
varieties of oats were assigned randomly to the three plots in each
block and four concentrations of fertilizer (measured as nitrogen
concentration) were randomly assigned to the subplots in each plot.
The yield on each subplot is the response shown in
Figure~\ref{fig:Oatsxy}.
\begin{figure}[tbp]
  \centerline{\includegraphics[width=\textwidth]{Implementation-Oatsxy}}
  \caption{Yield of oats versus applied concentration of nitrogen
    fertilizer for three different varieties of oats in 6 different
    locations.}
  \label{fig:Oatsxy}
\end{figure}

The fitted model \code{Om1}
<<Om1>>=
print(Om1 <- lmer2(yield ~ nitro + Variety + (1|Block/Variety), Oats), corr = FALSE)
@
provides fixed effects for the nitrogen concentration and for the
varieties (coded as differences relative to the reference variety
``Golden Rain'') and random effects for each block and for each plot
within the block.  In this case a plot can be indexed by the
combination of variety and block, denoted
\code{Variety:Block} in the output.  Notice that there are 18 levels of this
grouping factor corresponding to the 18 unique combinations of variety
and block.

A given plot occurs in one and only one block.  We say that the plot
grouping factor is \emph{nested within} the block grouping factor.
The structure of the matrices $\bm{A}$ and $\bm{L}$ for this model
(Figure~\ref{fig:Om1struct})
<<Om1struct,fig=TRUE,echo=FALSE,height=3,width=6>>=
print(image(Om1@A, colorkey = FALSE, aspect = 1),
      split = c(1,1,2,1), more = TRUE)
print(image(Om1@L, colorkey = FALSE, aspect = 1),
      split = c(2,1,2,1))
@
\begin{figure}[tbp]
  \centerline{\includegraphics[width=\textwidth]{Implementation-Om1struct}}
  \caption{Structure of the sparse matrices $\bm{A}$ (left panel) and
    $\bm{L}$ (right panel) for the model \code{Om1}.}
    \label{fig:Om1struct}
\end{figure}
In the matrix $\bm{A}$ the first $18$ rows and columns correspond to
the $18$ random effects ($1$ random effect for each of the $18$ levels
of this grouping factor).  The next $6$ rows and columns correspond to
the $6$ random effects for block ($6$ levels and $1$ random effect per
level). The off-diagonal elements in rows $19$ to $24$ and columns $1$
to $18$ indicate which plots and blocks are observed simultaneously.
Because the plot grouping factor is nested within the block grouping
factor there will be exactly one nonzero in the rows $19$ to $24$ for
each of the columns $1$ to $18$.

For this model the fixed-effects specification includes indicator
vectors with systematic zeros.  These appear as systematic zeros in
rows $27$ and $28$ of $\bm{A}$ and $\bm{L}$.  The statistical analysis
of model \code{Om1} indicates that the systematic effect of the
\code{Variety} factor is not significant and we could omit it from the
model, leaving us with
<<Om1a>>=
print(Om1a <- lmer2(yield ~ nitro + (1|Block/Variety), Oats), corr = FALSE)
@
with matrices $\bm{A}$ and $\bm{L}$ whose patterns are shown in
Figure~\ref{fig:Om1astruct}.
<<Om1astruct,fig=TRUE,echo=FALSE,height=3,width=6>>=
print(image(Om1a@A, colorkey = FALSE, aspect = 1),
      split = c(1,1,2,1), more = TRUE)
print(image(Om1a@L, colorkey = FALSE, aspect = 1),
      split = c(2,1,2,1))
@
\begin{figure}[tbp]
  \centerline{\includegraphics[width=\textwidth]{Implementation-Om1astruct}}
  \caption{Structure of the sparse matrices $\bm{A}$ (left panel) and
    $\bm{L}$ (right panel) for the model \code{Om1a}.}
    \label{fig:Om1astruct}
\end{figure}

In Figures~\ref{fig:Om1struct} and \ref{fig:Om1astruct} the pattern in
$\bm{L}$ is different from that of the lower triangle of $\bm{A}$ but
only because a permutation has been applied to the rows and columns of
$\bm{A}^*$ before computing the Cholesky decomposition.  The effect of
this permutation is to isolate connected blocks of rows and columns
close to the diagonal.

The isolation of connected blocks close to the diagonal is perhaps
more obvious when the model multiple random-effects expressions based on
the same grouping factor.  This construction is used to model
independent random effects for each level of the grouping factor.

For example, the random effect for the intercept and the random effect
for the slope in the sleep-study data could reasonably be modeled as
independent, as in the model
<<sm2>>=
print(sm2 <- lmer2(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy), corr = FALSE)
@

The structures of the matrices $\bm{A}$ and $\bm{L}$ for this model are
shown in Figure~\ref{fig:sm2struct}.
<<sm2struct,fig=TRUE,echo=FALSE,height=3,width=6>>=
print(image(sm2@A, colorkey = FALSE, aspect = 1),
      split = c(1,1,2,1), more = TRUE)
print(image(sm2@L, colorkey = FALSE, aspect = 1),
      split = c(2,1,2,1))
@
\begin{figure}[tbp]
  \centerline{\includegraphics[width=\textwidth]{Implementation-sm2struct}}
  \caption{Structure of the sparse matrices $\bm{A}$ (left panel) and
    $\bm{L}$ (right panel) for the model \code{sm2}.}
    \label{fig:sm2struct}
\end{figure}

The first $18$ elements of $\bm{b}$ are the random effects for the
intercept for each of the $18$ subjects followed by the random effects
for the slopes for each of the $18$ subjects.  The (0-based)
permutation vector applied to the rows and columns of $\bm{A}^*$
before taking the decomposition is
<<sm2perm>>=
str(sm2@L@perm)
@
This means that, in the 1-based indexing system used in R, the
permutation will pair up the first and $19$th rows and columns, the $2$nd and
$20$th rows and columns, etc. resulting in the pattern for $\bm{L}$
shown in Figure~\ref{fig:sm2struct}

Figure~\ref{fig:Oatsxy} indicates that the slope of yield versus
nitrogen concentration may depend on the block but not the plot within
the block.  We can fit such a model as
<<Om2>>=
print(Om2 <- lmer2(yield ~ nitro + (1|Variety:Block) + (nitro|Block), Oats), corr = FALSE)
@
The structures of the matrices $\bm{A}$ and $\bm{L}$ for this
model are shown in Figure~\ref{fig:Om2struct}.
<<Om2struct,fig=TRUE,echo=FALSE,height=3,width=6>>=
print(image(Om2@A, colorkey = FALSE, aspect = 1),
      split = c(1,1,2,1), more = TRUE)
print(image(Om2@L, colorkey = FALSE, aspect = 1),
      split = c(2,1,2,1))
@
\begin{figure}[tbp]
  \centerline{\includegraphics[width=\textwidth]{Implementation-Om2struct}}
  \caption{Structure of the sparse matrices $\bm{A}$ (left panel) and
    $\bm{L}$ (right panel) for the model \code{Om2}.}
    \label{fig:Om2struct}
\end{figure}

We see that the only difference in the structure of the $\bm{A}$
matrices from models \code{Om1a} and \code{Om2} is that rows and
columns $19$ to $24$ from model \code{Om1a} have been replicated.
Thus the $1\times 1$ blocks on the diagonal of $\bm{A}$ in positions
$19$ to $24$ for model \code{Om1a} become $2\times 2$ blocks in
model \code{Om2}.

This replication of rows associated with levels of the \code{Block}
factor carries over to the matrix $\bm{L}$.

The property of being nested or not is often attributed to random
effects.  In fact, nesting is a property of the grouping factors with
whose levels the random effects are associated.  In both models
\code{Om1a} and \code{Om2} the \code{Variety:Block} factor is nested
within \code{Block}.  If the grouping factors in the random effects
terms in a model form a nested sequence then the matrix $\bm{A}^*$ and
its Cholesky decomposition $\bm{L}$ will have the property that the
number of nonzeros in $\bm{L}$ is the same as the number of nonzeros
in the lower triangle of $\bm{A}^*$.  That is, there will be no
``fill-in'' generating new nonzero positions when forming the Cholesky
decomposition.

To check this we can examine the number of nonzero elements in
$\bm{A}$ and $\bm{L}$ for these models.  Because the matrix $\bm{A}$
is stored as a symmetric matrix with only the non-redundant elements
being stored explicitly, the number of stored nonzeros in these two
matrices are identical.
<<Om2ALsize>>=
length(Om2@A@x)
length(Om2@L@x)
@

\section{Non-nested grouping factors}
\label{sec:non-nested}

When grouping factors are not nested they are said to be ``crossed''.
Sometimes we will distinguish between \code{partially crossed}
grouping factors and \code{completely crossed} grouping factors.  When
two grouping factors are completely crossed, every level of the first
factor occurs at least once with every level of the second factor -
creating matrices $\bm{A}$ and $\bm{L}$ with dense off-diagonal blocks.

In observational studies it is more common to encounter partially
crossed grouping factors.  For example, the \code{ScotsSec} data in
the \code{mlmRev} package provides the attainment scores of 3435
students in Scottish secondary schools as well as some demographic
information on the students and an indicator of which secondary school
and which primary school the student attended.
<<ScotsSec>>=
str(ScotsSec)
@

If we use both \code{primary} and \code{second} as grouping factors
for random effects in a model the only possibility for these factors
to form a nested sequence is to have \code{primary} nested within
\code{second} (because there are 148 levels of \code{primary} and 19 levels
of \code{second}).  We could check if these are nested by doing a
cross-tabulation of these factors but it is easier to fit an initial model
<<Sm1>>=
print(Sm1 <- lmer2(attain ~ verbal * sex + (1|primary) + (1|second), ScotsSec), corr = FALSE)
@
and examine the \code{"isNested"} element of the \code{dims} slot.
<<Sm1dims>>=
Sm1@dims
@
We see that these grouping factors are not nested.  That is, some of
the elementary schools sent students to more than one secondary school.

Now that we know the answer we can confirm it by checking the first
few rows of the cross-tabulation
<<Scotscrosstab>>=
head(xtabs(~ primary + second, ScotsSec))
@
We see that primary schools 1, 4 and 6 each occurred with multiple secondary schools.

For non-nested grouping factors like these, the structure of $\bm{A}$
and $\bm{L}$, shown in Figure~\ref{fig:Sm1struct}
<<Sm1struct,fig=TRUE,echo=FALSE,height=3,width=6>>=
print(image(Sm1@A, colorkey = FALSE, aspect = 1),
      split = c(1,1,2,1), more = TRUE)
print(image(Sm1@L, colorkey = FALSE, aspect = 1),
      split = c(2,1,2,1))
@
\begin{figure}[tbp]
  \centerline{\includegraphics[width=\textwidth]{Implementation-Sm1struct}}
  \caption{Structure of the sparse matrices $\bm{A}$ (left panel) and
    $\bm{L}$ (right panel) for the model \code{Sm1}.}
    \label{fig:Sm1struct}
\end{figure}
is more complex than for nested grouping factors.  The matrix $\bm{A}$
has a $148\times 148$ diagonal block in the upper left, corresponding
the the $148$ levels of the \code{primary} factor, followed on the
diagonal by a $19\times 19$ diagonal block corresponding to the $19$
levels of the \code{second} factor.  However, the off-diagonal block
in rows $149$ to $167$ and columns $1$ to $148$ does not have a simple
structure. There is an indication of three groups of primary and
secondary schools but even those groups are not exclusive.

With non-nested grouping factors such as these there can be fill-in.
That is, the number of nonzeros in $\bm{L}$ is greater than the number
of non-redundant nonzeros in $\bm{A}$.
<<Sm1fill>>=
c(A = length(Sm1@A@x), L = length(Sm1@L@x))
@
The permutation applied to the rows and columns of $\bm{A}$ is a
``fill-reducing'' permutation chosen to reduce the amount of fill-in
during the Cholesky decomposition.  The approximate minimal degree
(AMD) algorithm \citep{davis06:csparse_book} is used to select this
permutation when non-nested grouping factors are detected.  It is
followed by a ``post-ordering'' permutation that isolates connected
blocks on the diagonal.


\section{Structure of $\bm{\Sigma}$ and $\bm{Z}$}
\label{sec:role-group-fact}

The columns of $\bm{Z}$ and the rows and columns of $\bm{\Sigma}$ are
associated with the levels of one or more grouping factors in the
data.  For example, a common application of linear mixed models is the
analysis of students' scores on the annual state-wide performance
tests mandated by the No Child Left Behind Act.  A given score is
associated with a student, a teacher, a school and a school district.
These could all be grouping factors in a model.

We write the grouping factors as $\bm{f}_i,i=1,\dots k$.  The number
of levels of the $i$th factor, $\bm{f}_i$, is $n_i$ and the number of
random effects associated with each level is $q_i$.  For example, if
$\bm{f}_1$ is ``student'' then $n_1$ is the number of students in the
study.  If we have a simple additive random effect for each student
then $q_1=1$.  If we have a random effect for both the intercept and
the slope with respect to time for each student then $q_1=2$.  The
$q_i,i=1,\dots,k$ are typically very small whereas the
$n_i,i=1,\dots,k$ can be very large.

In the statistical model we assume that random effects associated with
different grouping factors are independent, which implies that
$\bm{\Sigma}$ is block diagonal with $k$ diagonal blocks of sizes $n_i
q_i\times n_i q_i, i = 1,\dots,k$.  That is
\begin{equation}
  \label{eq:blockDiag}
  \bm{\Sigma}=
  \begin{bmatrix}
    \bm{\Sigma}_1 & \bm{0} & \hdots & \bm{0}\\
    \bm{0} & \bm{\Sigma}_2 & \hdots & \bm{0}\\
    \vdots & \vdots        & \ddots & \vdots \\
    \bm{0} & \bm{0}        & \hdots & \bm{\Sigma}_k
  \end{bmatrix}
\end{equation}
Furthermore, random effects associated
with different levels of the same grouping factor are assumed to be
independent and identically distributed, which implies that
$\bm{\Sigma}_i$ is itself block diagonal in $n_i$ blocks and that each
of these blocks is a copy of a $q_i\times q_i$ matrix
$\tilde{\bm{\Sigma}}_i$.  That is
\begin{equation}
  \label{eq:blockblock}
  \bm{\Sigma}_i=
    \begin{bmatrix}
    \tilde{\bm{\Sigma}}_i & \bm{0}     & \hdots & \bm{0}\\
    \bm{0}     & \tilde{\bm{\Sigma}}_i  & \hdots & \bm{0}\\
    \vdots    & \vdots    & \ddots & \vdots\\
    \bm{0}     & \bm{0}     & \hdots & \tilde{\bm{\Sigma}}_i
  \end{bmatrix}=
  \bm{I}_{n_i}\otimes\tilde{\bm{\Sigma}}_i\quad i=1,\dots,k
\end{equation}
where $\otimes$ denotes the Kronecker product.

The condition that $\bm{\Sigma}$ is positive semi-definite holds if
and only if the $\tilde{\bm{\Sigma}}_i,i=1,\dots,k$  are positive
semi-definite.  To ensure that the $\tilde{\bm{\Sigma}}_i$ are
positive semi-definite, we express them as
\begin{equation}
  \label{eq:STinner}
  \tilde{\bm{\Sigma}}_i=
  \tilde{\bm{T}}_i\tilde{\bm{S}}_i\tilde{\bm{S}}_i\tilde{\bm{T}}_i\trans,\quad
  i=1,\dots,k
\end{equation}
where $\tilde{\bm{T}}_i$ is a $q_i\times q_i$ unit lower-triangular
matrix (i.e. all the elements above the diagonal are zero and all the
diagonal elements are unity) and $\tilde{\bm{S}}_i$ is a $q_i\times
q_i$ diagonal matrix with non-negative elements on the diagonal.

This is the ``LDL'' form of the Cholesky decomposition of positive
semi-definite matrices except that we express the diagonal matrix
$\bm{D}$, which is on the variance scale, as the square of the
diagonal matrix $\bm{S}$, which is on the standard deviation scale.
The profiled deviance behaves more like a quadratic on the standard
deviation scale than it does on the variance scale so the use of the
standard deviation scale enhances convergence.

The $n_i q_i\times n_i q_i$ matrices $\bm{S}_i,\bm{T}_i,\,i=1,\dots,k$
and the $q\times q$ matrices $\bm{S}$ and $\bm{T}$ are defined
analogously to (\ref{eq:blockblock}) and (\ref{eq:blockDiag}).  In
particular,
\begin{align}
  \bm{S}_i&=\bm{I}_{n_i}\otimes\tilde{\bm{S}}_i,\quad i=1,\dots,k \\
  \bm{T}_i&=\bm{I}_{n_i}\otimes\tilde{\bm{T}}_i,\quad i=1,\dots,k
\end{align}

Note that when $q_i=1$, $\tilde{\bm{T}}_i=\bm{I}$ and hence
$\bm{T}_i=\bm{I}$.  Furthermore, $\bm{S}_i$ is a multiple of the
identity matrix in this case.

The parameter vector $\bm{\theta}_i,i=1,\dots,k$ consists of the $q_i$
diagonal elements of $\tilde{\bm{S}}_i$, which are constrained to be
non-negative, followed by the $q_i(q_i-1)/2$ elements in the strict
lower triangle of $\tilde{\bm{T}}_i$ (in column-major ordering).
These last $q_i(q_i-1)/2$ elements are unconstrained.  The
$\bm{\theta}_i$ are combined as
\begin{displaymath}
  \bm{\theta}=
  \begin{bmatrix}
    \bm{\theta}_1 \\ \bm{\theta}_2 \\ \vdots \\ \bm{\theta}_k
  \end{bmatrix} .
\end{displaymath}
Each of the $q\times q$ matrices $\bm{S}$, $\bm{T}$ and $\bm{\Sigma}$
in the decomposition $\bm{\Sigma}=\bm{T}\bm{S}\bm{S}\bm{T}\trans$ is a
function of $\bm{\theta}$.

As a unit triangular matrix $\bm{T}$ is non-singular.  That is,
$\bm{T}^{-1}$ exists and is easily calculated from the
$\tilde{\bm{T}}_i^{-1},i=1,\dots,k$.  When $\bm{\theta}$ is not on the
boundary defined by the constraints, $\bm{S}$ is a
diagonal matrix with strictly positive elements on the diagonal,
which implies that $\bm{S}^{-1}$ exists and that
$\bm{\Sigma}$ is non-singular with
$\bm{\Sigma}^{-1}=\bm{T}\invtrans\bm{S}^{-1}\bm{S}^{-1}\bm{T}^{-1}$.

When $\bm{\theta}$ is on the boundary the matrices $\bm{S}$
and $\bm{\Sigma}$ exist but are not invertible.  We say that
$\bm{\Sigma}$ is a \emph{degenerate} variance-covariance matrix in the
sense that one or more linear combinations of the vector $\bm{b}$ are
defined to have zero variance.  That is, the distribution of these
linear combinations is a point mass at 0.

The maximum likelihood estimates of $\bm{\theta}$ (or the restricted
maximum likelihood estimates, defined below) can be located on the
boundary.  That is, they can correspond to a degenerate
variance-covariance matrix and we must be careful to allow for this
case.  However, to begin we consider the non-degenerate case.

\section{Methods for non-singular $\bm{\Sigma}$}
\label{sec:non-singular}

When $\bm{\theta}$ is not on the boundary we can define a
standardized random effects vector
\begin{equation}
  \label{eq:bstar}
  \bm{b}^*=\bm{S}^{-1}\bm{T}^{-1}\bm{b}
\end{equation}
with the properties
\begin{align}
  \mathsf{E}[\bm{b}^*]&=\bm{S}^{-1}\bm{T}^{-1}\mathsf{E}[\bm{b}]\\
  \mathsf{Var}[\bm{b}^*]=\bm{0}&
  \begin{aligned}[t]
    &=\mathsf{E}[\bm{b}^*{\bm{b}^*}\trans]\\
    &=\bm{S}^{-1}\bm{T}^{-1}\mathsf{Var}[\bm{b}]\bm{T}\invtrans\bm{S}^{-1}\\
    &=\sigma^2\bm{S}^{-1}\bm{T}^{-1}\bm{\Sigma}\bm{T}\invtrans\bm{S}^{-1}\\
    &=\sigma^2\bm{S}^{-1}\bm{T}^{-1}\bm{T}\bm{S}\bm{S}\bm{T}\trans\bm{T}\invtrans\bm{S}^{-1}\\
    &=\sigma^2\bm{I}.
  \end{aligned}
\end{align}
Thus, the unconditional distribution of the $q$ elements of
$\bm{b}^*$ is
$\bm{b}^*\sim\mathcal{N}\left(\bm{0},\sigma^2\bm{I}\right)$, like that
of the $n$ elements of $\bm{\epsilon}$.

Obviously the transformation from $\bm{b}^*$ to $\bm{b}$ is
\begin{equation}
  \label{eq:bstarb}
  \bm{b}=\bm{T}\bm{S}\bm{b}^*
\end{equation}
and the $n\times q$ model matrix for $\bm{b}^*$ is
\begin{equation}
  \label{eq:Zstar}
  \bm{Z}^*=\bm{Z}\bm{T}\bm{S}
\end{equation}
so that
\begin{equation}
  \label{eq:Zb}
  \bm{Z}^*\bm{b}^*=\bm{Z}\bm{T}\bm{S}\bm{S}^{-1}\bm{T}^{-1}\bm{b}=\bm{Z}\bm{b} .
\end{equation}

Notice that $\bm{Z}^*$ can be evaluated even when
$\bm{\theta}$ is on the boundary.  Also, if we have a value of
$\bm{b}^*$ in such a case, we can evaluated $\bm{b}$ from $\bm{b}^*$.

Given the data $\bm{y}$ and values of $\bm{\theta}$ and $\bm{\beta}$,
the mode of the conditional distribution of $\bm{b}^*$ is the solution
to a penalized least squares problem
\begin{equation}
  \label{eq:bstarmode}
  \begin{split}
    \tilde{\bm{b}}^*(\bm{\theta},\bm{\beta}|\bm{y})&=
    \arg\min_{\bm{b}^*} \left[
    \left\|\bm{y}-\bm{X}\bm{\beta}-\bm{Z}^*\bm{b}^*\right\|^2
    +{\bm{b}^*}\trans\bm{b}^*\right]\\
    &=
    \arg\min_{\bm{b}^*}
    \left\|
      \begin{bmatrix}
        \bm{y}\\
        \bm{0}
      \end{bmatrix} -
      \begin{bmatrix}
        \bm{Z}^* & \bm{X}\\
        \bm{I}   & \bm{0}
      \end{bmatrix}
      \begin{bmatrix}
        \bm{b}^*\\
        \bm{\beta}
      \end{bmatrix}
      \right\|^2 .
  \end{split}
\end{equation}
In fact, if we optimize the penalized least squares expression in
(\ref{eq:bstarmode}) with respect to both $\bm{b}$ and $\bm{\beta}$ we
obtain the conditional estimates
$\widehat{\bm{\beta}}(\bm{\theta}|\bm{y})$ and the conditional modes
$\tilde{\bm{b}}^*(\bm{\theta},\widehat{\bm{\beta}}(\bm{\theta})|\bm{y}))$
which we write as $\widehat{\bm{b}}^*(\bm{\theta})$.  That is,
\begin{equation}
  \label{eq:bstarbeta}
  \begin{split}
    \begin{bmatrix}
      \widehat{\bm{b}^*}(\bm{\theta})\\
      \widehat{\bm{\beta}}(\bm{\theta})
    \end{bmatrix} & =
    \arg\min_{\bm{b}^*,\bm{\beta}}
    \left\|
      \begin{bmatrix}
        \bm{Z}^* & \bm{X} & -\bm{y}\\
        \bm{I}   & \bm{0} &  \bm{0}
      \end{bmatrix}
      \begin{bmatrix}
        \bm{b}^*\\
        \bm{\beta}\\
        1
      \end{bmatrix}
    \right\|^2\\
     & =
    \arg\min_{\bm{b}^*,\bm{\beta}}
    \begin{bmatrix}
      \bm{b}^*\\
      \bm{\beta}\\
      1
    \end{bmatrix}\trans
    \bm{A}^*(\bm{\theta})
    \begin{bmatrix}
      \bm{b}^*\\
      \bm{\beta}\\
      1
    \end{bmatrix}
  \end{split}
\end{equation}
where the matrix $\bm{A}^*(\bm{\theta})$ is as shown in (\ref{eq:Astar})
and
\begin{equation}
  \label{eq:Amatrix}
  \bm{A}=
  \begin{bmatrix}
    \bm{Z}\trans\bm{Z} & \bm{Z}\trans\bm{X} & -\bm{Z}\trans\bm{y} \\
    \bm{X}\trans\bm{Z} & \bm{X}\trans\bm{X} & -\bm{X}\trans\bm{y} \\
    -\bm{y}\trans\bm{Z} & -\bm{y}\trans\bm{X} & \bm{y}\trans\bm{y} \\
  \end{bmatrix} .
\end{equation}
Note that $\bm{A}$ does not depend upon $\bm{\theta}$.  Furthermore,
the nature of the model matrices $\bm{Z}$ and $\bm{X}$ ensures that
the pattern of nonzeros in $\bm{A}^*(\bm{\theta})$ is the same as that
in $\bm{A}$.

Let the $q\times q$ permutation matrix $\bm{P}_Z$ represent a
fill-reducing permutation for $\bm{Z}\trans\bm{Z}$ and $\bm{P}_X$, of
size $p\times p$, represent a fill-reducing permutation for
$\bm{X}\trans\bm{X}$.  These could be determined, for example, using
the \emph{approximate minimal degree} (AMD) algorithm described in
\citet{davis06:csparse_book} and \citet{Davis:1996} and implemented in
both the \texttt{\small Csparse} \citep{Csparse} and the
\texttt{\small CHOLMOD} \citep{Cholmod} libraries of C functions.  (In
many cases $\bm{X}\trans\bm{X}$ is dense, but of small dimension
compared to $\bm{Z}\trans\bm{Z}$, and $\bm{Z}\trans\bm{X}$ is nearly
dense so $\bm{P}_X$ can be $\bm{I}_p$, the $p\times p$ identity
matrix.)

Let the permutation matrix $\bm{P}$ be
\begin{equation}
  \label{eq:fillPerm}
  \bm{P}=
  \begin{bmatrix}
    \bm{P}_Z & \bm{0} & \bm{0} \\
    \bm{0} & \bm{P}_X & \bm{0} \\
    \bm{0} & \bm{0} & 1
  \end{bmatrix}
\end{equation}
and $\bm{L}(\bm{\theta})$ be the sparse Cholesky decomposition of
$\bm{A}^*(\bm{\theta})$ relative to this permutation.  That
is, $\bm{L}(\bm{\theta})$ is a sparse lower triangular matrix with the
property that
\begin{equation}
  \label{eq:Lmat}
  \bm{L}(\bm{\theta})\bm{L}(\bm{\theta})\trans=
  \bm{P}\bm{A}^*(\bm{\theta})\bm{P}\trans
\end{equation}

For $\bm{L}(\bm{\theta})$ to exist we must ensure that
$\bm{A}^*(\bm{\theta})$ is positive definite.  Examination of
(\ref{eq:bstarbeta}) shows that
this will be true if
$\bm{X}$ is of full column rank and $\bm{y}$ does not lie in the
column span of $\bm{X}$ (or, in statistical terms, if we can't fit
$\bm{y}$ perfectly using only the fixed effects).

Let $r>0$ be the last element on the diagonal of $\bm{L}$.  Then the
minumum penalized residual sum of squares in (\ref{eq:bstarbeta}) is
$r^2$ and it occurs at $\widehat{\bm{b}^*}(\bm{\theta})$ and
$\hat{\beta}(\bm{\theta})$, the solutions to the sparse triangular system
\begin{equation}
  \label{eq:soln}
  \bm{L}(\bm{\theta})\trans\bm{P}
  \begin{bmatrix}
    \widehat{\bm{b}^*}(\bm{\theta})\\
    \widehat{\bm{\beta}}(\bm{\theta})\\
    1
  \end{bmatrix}=
  \begin{bmatrix}
    \bm{0}\\
    \bm{0}\\
    r
  \end{bmatrix}
\end{equation}
(Technically we should not write the $1$ in the solution; it should be
an unknown.  However, for $\bm{L}$ lower triangular with $r$ as the last
element on the diagonal and $\bm{P}$ a permutation that does not move
the last row, the solution for this ``unknown'' will always be $1$.)
Furthermore, $\log|{\bm{Z}^*}\trans\bm{Z}+\bm{I}|$ can be evaluated as
the sum of the logarithms of the first $q$ diagonal elements of
$\bm{L}(\bm{\theta})$.

The \emph{profiled deviance function},
$\tilde{\mathcal{D}}(\bm{\theta})$, which is negative twice the
log-likelihood of model (\ref{eq:lmm}) evaluated at
$\bm{\Sigma}(\bm{\theta})$, $\widehat{\bm{\beta}}(\bm{\theta})$ and
$\hat{\sigma}^2(\bm{\theta})$, can be expressed as
\begin{equation}
  \label{eq:dtheta}
  \tilde{\mathcal{D}}(\bm{\theta})=
  \log\left|{\bm{Z}^*}\trans\bm{Z}^*+\bm{I}\right|+
  n\left(1+\log\frac{2\pi r^2}{n}\right) .
\end{equation}

Notice that it is not necessary to solve for
$\widehat{\bm{\beta}}(\bm{\theta})$ or $\widehat{\bm{b}}^*(\bm{\theta})$
or $\widehat{\bm{b}}(\bm{\theta})$ to be able to evaluate
$d(\bm{\theta})$.  All that is needed is to update $\bm{A}$ to form
$\bm{A}^*$ from which the sparse Cholesky decomposition
$\bm{L}(\bm{\theta})$ can be calculated and
$\tilde{\mathcal{D}}(\bm{\theta})$ evaluated.

Once $\hat{\bm{\theta}}$ is determined we can solve for
$\widehat{\bm{\beta}}(\hat{\bm{\theta}})$
and $\widehat{\bm{b}}^*(\bm{\theta})$ using (\ref{eq:soln}) and for
\begin{equation}
  \label{eq:3}
  \widehat{\sigma}^2(\hat{\bm{\theta}})=\frac{r^2(\hat{\bm{\theta}})}{n} .
\end{equation}
Furthermore,
$\widehat{\bm{b}}(\hat{\bm{\theta}})=
\bm{S}\bm{T}\widehat{\bm{b}}^*(\hat{\bm{\theta}})$.

\section{Methods for singular $\bm{\Sigma}$}
\label{sec:singular}

When $\bm{\theta}$ is on the boundary, corresponding to a singular
$\bm{\Sigma}$, some of the columns of $\bm{Z}^*$ are zero.  However,
the matrix $\bm{A}^*$ is non-singular and elements of $\bm{b}^*$
corresponding to the zeroed columns in $\bm{Z}^*$ approach zero
smoothly as $\bm{\theta}$ approaches the boundary.  Thus
$r(\bm{\theta})$ and $\left|{\bm{Z}^*}\trans\bm{Z}+\bm{I}\right|$ are
well-defined, as are $\tilde{\mathcal{D}}(\bm{\theta})$ and the
conditional modes $\widehat{\bm{b}}(\bm{\theta})$.

In other words, (\ref{eq:Astar}) and (\ref{eq:Lmat}) can be used to
define $\tilde{\mathcal{D}}(\bm{\theta})$ whether or not $\bm{\theta}$
is on the boundary.

\section{REML estimates}
\label{sec:reml-estimates}

It is common to estimate the per-observation noise variance $\sigma^2$
in a fixed-effects linear model as $\hat{\sigma}^2=r^2/(n-p)$ where
$r^2$ is the (unpenalized) residual sum-of-squares, $n$ is the number
of observations and $p$ is the number of fixed-effects parameters.
This is not the maximum likelihood estimate of $\sigma^2$, which is
$r^2/n$.  It is the ``restricted'' or ``residual'' maximum likelihood
(REML) estimate, which takes into account that the residual vector
$\bm{y}-\hat{\bm{y}}$ is constrained to a linear subspace of dimension
$n-p$ in the response space.  Thus its squared length,
$\left\|\bm{y}-\hat{\bm{y}}\right\|^2=r^2$, has only $n-p$
\emph{degrees of freedom} associated with it.

The profiled REML deviance for a linear mixed model can be expressed as
\begin{equation}
  \label{eq:REMLdev}
  \tilde{\mathcal{D}}_R(\bm{\theta})=
  \log\left|{\bm{Z}^*}\trans\bm{Z}^*+\bm{I}\right|+
  \log\left|\bm{L}_{\bm{X}}\right|^2+
  (n-p)\left(1+\log\frac{2\pi r^2}{n-p}\right) .
\end{equation}

\section{Generalized linear mixed models}
\label{sec:GLMMs}

\subsection{Generalized linear models}
\label{sec:gener-line-models}

As described in \citet{mccullagh89:_gener_linear_model}, a generalized
linear model is a statistical model in which the \emph{linear
  predictor} for the $i$th response, $\eta_i=\bm{x}_i\bm{\beta}$ where
$\bm{x}_i$ is the $i$th row of the $n\times p$ model matrix $\bm{X}$ derived
from the form of the model and the values of any covariates, is
related to the \emph{expected value of the response}, $\mu_i$, through
an invertible \emph{link function}, $g$.  That is
\begin{equation}
  \label{eq:glmlink}
  \bm{x}_i\bm{\beta}=\eta_i=g(\mu_i)\quad i=1,\dots,n
\end{equation}
and
\begin{equation}
  \label{eq:glmlinkinv}
  \mu_i=g^{-1}(\eta_i)=g^{-1}(\bm{x}_i\bm{\beta})\quad i=1,\dots,n
\end{equation}

When the distribution of $y_i$ given $\mu_i$ is from the exponential
family there exist a \emph{natural} link function for the family
\citep{mccullagh89:_gener_linear_model}. For a
binomial response the natural link is the \emph{logit} link defined as
\begin{equation}
  \label{eq:logit}
  \eta_i = g(\mu_i) = \log\left(\frac{\mu_i}{1-\mu_i}\right)\quad i=1,\dots,n
\end{equation}
with inverse link
\begin{equation}
  \label{eq:logitInv}
  \mu_i=g^{-1}(\eta_i)=\frac{1}{1+\exp(-\eta_i)}\quad i=1,\dots,n
\end{equation}
Because $\mu_i$ is the probability of the $i$th observation being a
``success'', $\eta_i$ is the log of the odds ratio.

The parameters $\bm{\beta}$ in a generalized linear model are generally
estimated by \emph{iteratively reweighted least squares} (IRLS).  At
each iteration in this algorithm the current parameter estimates are
replaced by the parameter estimates of a weighted least squares fit
with model matrix $\bm{X}$ to an adjusted dependent variable.  The weights
and the adjusted dependent variable are calculated from the link function and
the current parameter values.

\subsection{Generalized linear mixed models}
\label{sec:gener-line-mixed}

In a generalized linear mixed model (GLMM) the $n$-dimensional vector
of linear predictors, $\bm{\eta}$, incorporates both fixed effects,
$\bm{\beta}$, and random effects, $\bm{b}$, as
\begin{equation}
  \label{eq:glmmLP}
  \bm{\eta} = \bm{X}\bm{\beta}+\bm{Z}\bm{b}
\end{equation}
where $\bm{X}$ is an $n\times p$ model matrix and $\bm{Z}$ is an $n\times q$
model matrix.

As for linear mixed models,
we model the distribution of the random effects as a multivariate
normal (Gaussian) distribution with mean $\bm{0}$ and $q\times q$
variance-covariance matrix $\bm{\Sigma}$.  That is,
\begin{equation}
  \label{eq:random-effects-dist}
  \bm{b}\sim\mathcal{N}\left(\bm{0},\bm{\Sigma}(\bm{\theta})\right) .
\end{equation}

The maximum likelihood estimates $\hat{\bm{\beta}}$ and $\hat{\bm{\theta}}$
maximize the likelihood of the parameters, $\bm{\beta}$ and $\bm{\theta}$, given
the observed data, $\bm{y}$.  This likelihood is numerically equivalent to
the marginal density of $\bm{y}$ given $\bm{\beta}$ and $\bm{\theta}$, which is
\begin{equation}
  \label{eq:marginalDens}
  f(\bm{y}|\bm{\beta},\bm{\theta}) =
  \int_{\bm{b}}p(\bm{y}|\bm{\beta},\bm{b})f(\bm{b}|\bm{\Sigma}(\bm{\theta}))\,d\bm{b}
\end{equation}
where $p(\bm{y}|\bm{\beta},\bm{b})$ is the probability mass function of $\bm{y}$,
given $\bm{\beta}$ and $\bm{b}$, and $f(\bm{b}|\bm{\Sigma})$ is the (Gaussian)
probability density at $\bm{b}$.

Unfortunately the integral in (\ref{eq:marginalDens}) does not have a
closed-form solution when $p(\bm{y}|\bm{\beta},\bm{b})$ is binomial.  However,
we can approximate this integral quite accurately using a \emph{Laplace}
approximation.  For given values of $\bm{\beta}$ and $\bm{\theta}$ we
determine the \emph{conditional modes} of the random effects
\begin{equation}
  \label{eq:conditionalmodes}
  \tilde{\bm{b}}(\bm{\beta},\bm{\theta})=
  \arg\max_{\bm{b}}p(\bm{y}|\bm{\beta},\bm{b})f(\bm{b}|\bm{\Sigma}(\bm{\theta})) ,
\end{equation}
which are the values of the random effects that maximize the
conditional density of the random effects given the data and the model
parameters.  The conditional modes can be determined by a penalized
iteratively reweighted least squares algorithm (PIRLS, see
\S\ref{sec:deta-pirls-algor}) where the contribution of the fixed
effects parameters, $\bm{\beta}$, is incorporated as an offset,
$\bm{X}\bm{\beta}$, and the contribution of the variance components,
$\bm{\theta}$, is incorporated as a penalty term in the weighted least
squares fit.

At the conditional modes, $\tilde{\bm{b}}$, we evaluate the second order
Taylor series approximation to the log of the integrand (i.e.{} the log
of the conditional density of $\bm{b}$) and use its integral as an
approximation to the likelihood.

It is the Laplace approximation to the likelihood that is optimized to
obtain approximate values of the mle's for the parameters and the
corresponding conditional modes of the random effects vector $\bm{b}$.

\subsection{Details of the PIRLS algorithm}
\label{sec:deta-pirls-algor}

Recall from (\ref{eq:conditionalmodes}) that the conditional modes of
the random effects $\tilde{\bm{b}}(\bm{\beta},\bm{\theta},\bm{y})$ maximize the
conditional density of $\bm{b}$ given the data and values of the
parameters $\bm{\beta}$ and $\bm{\theta}$.  The penalized iteratively
reweighted least squares (PIRLS) algorithm for determining these
conditional modes combines characteristic of the iteratively
reweighted least squares (IRLS) algorithm for generalized linear
models~\citep[\S2.5]{mccullagh89:_gener_linear_model} and the
penalized least squares representation of a linear mixed
model~\citep{bates04:_linear}.

At the $r$th iteration of the IRLS algorithm the current value of the
vector of random effects. $\bm{b}^{(r)}$ (we use parenthesized
superscripts to denote the iteration) produces a linear predictor
\begin{equation}
  \label{eq:rthLinPred}
  \bm{\eta}^{(r)}=\bm{X}\bm{\beta}+\bm{Z}\bm{b}^{(r)}
\end{equation}
with corresponding mean vector $\bm{\mu}^{(r)}=\bm{g}^{-1}\bm{\eta}^{(r)}$.
(The vector-valued link and inverse link functions, $\bm{g}$ and
$\bm{g}^{-1}$, apply the scalar link and inverse link, $g$ and
$g^{-1}$, componentwise.)
A vector of weights and a vector of derivatives of the form
$d\eta/d\mu$ are also evaluated.  For convenience of notation we
express these as diagonal matrices, $\bm{W}^{(r)}$ and $\bm{G}^{(r)}$,
although calculations involving these quantities are performed
component-wise and not as matrices.

The adjusted dependent variate at iteration $r$ is
\begin{equation}
  \label{eq:adjdep}
  \bm{z}^{(r)}=\bm{\eta}^{(r)}+\bm{G}^{(r)}\left(\bm{y}-\bm{\mu}^{(r)}\right)
\end{equation}
from which the updated parameter, $\bm{b}^{(r+1)}$, is determined as the
solution to
\begin{equation}
  \label{eq:IRLSupdate}
  \bm{Z}\trans\bm{W}^{(r)}\bm{Z}\bm{b}^{(r+1)}=\bm{Z}\trans\bm{W}^{(r)}\bm{z}^{(r)} .
\end{equation}

\citet[\S2.5]{mccullagh89:_gener_linear_model} show that the IRLS
algorithm is equivalent to the Fisher scoring algorithm for any link
function and also equivalent to the Newton-Raphson algorithm when the
link function is the natural link for a probability distribution in
the exponential family.  That is, IRLS will minimize $-\log
p(\bm{y}|\bm{\beta},\bm{b})$ for fixed $\bm{\beta}$.  However, we wish to determine
\begin{equation}
  \label{eq:conditionalmin}
  \begin{aligned}
    \tilde{\bm{b}}(\bm{\beta},\bm{\theta})
    &= \arg\max_{\bm{b}}p(\bm{y}|\bm{\beta},\bm{b})f(\bm{b}|\bm{\Sigma}(\bm{\theta}))\\
    &= \arg\min_{\bm{b}}\left[-\log p(\bm{y}|\bm{\beta},\bm{b})
      +\frac{\bm{b}\trans\bm{\Sigma}^{-1}(\bm{\theta})\bm{b}}{2}\right] .
  \end{aligned}
\end{equation}
As shown in \citet{bate:debr:2004} we can incorporate the
contribution of the Gaussian distribution by adding $q$
``pseudo-observations'' with constant unit weights, observed values of
$0$ and predicted values of $\bm{\Delta}(\bm{\theta})\bm{b}$ where
$\bm{\Delta}$ is any $q\times q$ matrix such that
$\bm{\Delta}\trans\bm{\Delta}=\bm{\Sigma}^{-1}(\bm{\theta})$.

Thus the update in the penalized iteratively reweighted least squares
(PIRLS) algorithm for determining the conditional modes,
$\tilde{\bm{b}}(\bm{\beta},\bm{\theta},\bm{y})$, expresses $\bm{b}^{(r+1)}$ as the
solution to the penalized weighted least squares problem
\begin{equation}
  \label{eq:PIRLSupdate}
  \left(\bm{Z}\trans\bm{W}^{(r)}\bm{Z}+\bm{\Sigma}^{-1}\right)\bm{b}^{(r+1)}=
  \bm{Z}\trans\bm{W}^{(r)}\bm{z}^{(r)} .
\end{equation}
or the equivalent problem
\begin{equation}
  \label{eq:PIRLSbstarupdate}
  \left({\bm{Z}^*}\trans\bm{W}^{(r)}\bm{Z}^*+\bm{I}\right){\bm{b}^*}^{(r+1)}=
  {\bm{Z}^*}\trans\bm{W}^{(r)}\bm{z}^{(r)} .
\end{equation}
The sequence of iterates ${\bm{b}^*}^{(0)}, {\bm{b}^*}^{(1)},\dots$ is considered to
have converged to the conditional modes
$\tilde{\bm{b}}^*(\bm{\beta},\bm{\theta},\bm{y})$ when the relative change in the
linear predictors $\|\bm{\eta}^{(r+1)}-\bm{\eta}^{(r)}\|/\|\bm{\eta}^{(r)}\|$
falls below a threshold. The variance-covariance matrix of $\bm{b}^*$,
conditional on $\bm{\beta}$ and $\bm{\theta}$, is approximated as
\begin{equation}
  \label{eq:varcov}
  \mathrm{Var}\left(\bm{b}|\bm{\beta},\bm{\theta},\bm{y}\right)\approx\bm{D}\equiv
  \left({\bm{Z}^*}\trans\bm{W}^{(r)}\bm{Z}^*+\bm{I}\right)^{-1} .
\end{equation}
This approximation is analogous to using the inverse of Fisher's
information matrix as the approximate variance-covariance matrix for
maximum likelihood estimates.

\subsection{Details of the Laplace approximation}
\label{sec:deta-lapl-appr}

The Laplace approximation to the likelihood $L(\bm{\beta},\bm{\theta}|\bm{y})$ is
obtained by replacing the logarithm of the integrand in
(\ref{eq:marginalDens}) by its second-order Taylor series at the
conditional maximum, $\tilde{\bm{b}}(\bm{\beta},\bm{\theta})$.  On the scale of
the deviance (negative twice the log-likelihood) the approximation is
\begin{equation}
  \label{eq:LaplaceApprox}
  \begin{aligned}
    -2\ell(\bm{\beta},\bm{\theta}|\bm{y})
    &=-2\log\left\{\int_{\bm{b}}p(\bm{y}|\bm{\beta},\bm{b})f(\bm{b}|\bm{\Sigma}(\bm{\theta}))
      \,d\bm{b}\right\}\\
    &\approx 2\log\left\{\int_{\bm{b}}\exp\left\{-\frac{1}{2}
        \left[d(\bm{\beta},\tilde{\bm{b}},\bm{y})+
          {\tilde{\bm{b}}}\trans\tilde{\bm{b}}^*+
          +\bm{b}\trans\bm{D}^{-1}\bm{b}\right]\right\}\,d\bm{b}\right\}\\
    &=d(\bm{\beta},\tilde{\bm{b}},\bm{y})+{\tilde{\bm{b}}^*}{}\trans\tilde{\bm{b}}^*
    +\log|\bm{D}|
  \end{aligned}
\end{equation}
where $d(\bm{\beta},\bm{b},\bm{y})$ is the deviance function from the linear
predictor only.  That is, $d(\bm{\beta},\bm{b},\bm{y})=-2\log p(\bm{y}|\bm{\beta},\bm{b})$.
This quantity can be evalated as the sum of the deviance
residuals~\citep[\S2.4.3]{mccullagh89:_gener_linear_model}.


\bibliography{lme4}
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