\documentclass[article,shortnames,nojss]{jss}
%% need no \usepackage{Sweave.sty}

%pacakges
\usepackage[british]{babel}
\usepackage[latin1]{inputenc}
\usepackage{verbatim}
\usepackage{amsmath,amssymb,amsthm,amsfonts,amsbsy}
\usepackage{url}

\newcommand{\proper}{\mathsf}
%Define some distributions
\newcommand{\Normal}[2]{\proper{N}\left(#1,#2\right)}
% Use for points, vectors, and matrices:
\newcommand{\mv}[1]{{\boldsymbol{#1}}}
%transpose and integration infinitesimals
\newcommand{\trsp}{\ensuremath{\top}}
\newcommand{\md}{\ensuremath{\,\mathrm{d}}}
%median
\newcommand{\median}{\operatorname{median}}
%diag
\newcommand{\diag}{\operatorname{diag}}
%argmax
\newcommand{\argmax}[1]{\underset{#1}{\operatorname{argmax}}\ }
%norm
\newcommand{\norm}[1]{\left\lVert#1\right\rVert}
%determinant
\renewcommand{\det}[1]{\left\lvert#1\right\rvert}
%ordo
\newcommand{\ordo}[1]{\ensuremath{\mathcal{O}\left(#1\right)}}
%Caliographic M for model
\newcommand{\M}{\mathcal{M}}
%NOx and PM commands
\newcommand{\no}[1]{\ensuremath{\text{NO}_\text{#1}}}
\newcommand{\lno}[1]{\ensuremath{\log\ \no{#1}}}
\newcommand{\PM}[1]{\ensuremath{\text{PM}_{#1}}}
%predictions of the log-Gaussian process
\newcommand{\Zhat}[1]{\ensuremath{\widehat{Z^*_{#1}}}}
\newcommand{\Zhathat}[2]{\ensuremath{\widehat{\widehat{Z^*_{#2}}}^\text{\sc #1}}}

\newcommand{\zhat}[1]{\ensuremath{\widehat{z^*_{#1}}}}
\newcommand{\zhathat}[2]{\ensuremath{\widehat{\widehat{z^*_{#2}}}^\text{\sc #1}}}
%temporal average predictions of the log-Gaussian process
\newcommand{\zhatLTA}{\ensuremath{\widehat{ \overline{z^*} }(s)}}
\newcommand{\zhathatLTA}[1]{\ensuremath{\widehat{\widehat{ \overline{z^*} }}^\text{\sc #1}\!\!\!\!(s)}}
%define MSPE 
\newcommand{\MSPE}{\proper{MSPE}}

%\VignetteIndexEntry{SpatioTemporal: An R Package for Spatio-Temporal Modelling of Air-Pollution}

%% almost as usual
\author{
Johan Lindstr\"om\\ Lund University \& University of Washington \And
Adam Szpiro\\ University of Washington \AND
Paul D. Sampson\\ University of Washington \And
Silas Bergen\\ University of Washington \And
Lianne Sheppard\\ University of Washington}
\title{\pkg{SpatioTemporal}: An \proglang{R} Package for Spatio-Temporal Modelling of Air-Pollution}


%% for pretty printing and a nice hypersummary also set:
\Plainauthor{Johan Lindstr\"om, Adam Szpiro, Paul D. Sampson, Silas Bergen,
  Lianne Sheppard} %% comma-separated
\Plaintitle{SpatioTemporal: An R Package for Spatio-Temporal Modelling of
  Air-Pollution} %% without formatting
\Shorttitle{\pkg{SpatioTemporal}: Spatio-Temporal Modelling of 
  Air-Pollution} %% a short title (if necessary)

%% an abstract and keywords
\Abstract{
Modelling of Gaussian spatio-temporal processes provide ample opportunity for different
model formulations, however two principal directions have emerged. The data can be
modelled either as a set of spatially varying temporal basis functions or as
spatial fields evolving in time. This package provides maximum-likelihood
estimation and cross-validation tools for the first case.
Development of the package was motivated by the need to provide accurate
spatio-temporal predictions of ambient air pollution at small spatial scales for
a health effects study.

The package provides tools for extracting temporal basis functions from the
data. It handles incomplete and highly unbalanced spatio-temporal sampling designs
and allows for a flexible set of covariates and covariance structures to
capture the spatial variability in the temporal basis functions and in the
spatio-temporal residuals. Further, the package provides bias corrected
predictions for log-transformed data, cross-validation tools
and rudimentary MCMC-routines to asses the modelfit.

Here we describe the package, providing a brief summary of the theory, but
focusing our attention on an example illustrating how the package can be used
for model fitting and cross-validation analysis; the example is based on data
included in the package.
}
\Keywords{Spatio-temporal modelling, likelihood based estimation,
  cross-validation, air pollution, \no{x} smooth EOFs, $\log$-Gaussian process,
  unbalanced data}
\Plainkeywords{Spatio-temporal modelling, likelihood based estimation,
  cross-validation, air pollution, NOx, smooth EOFs, log-Gaussian process,
  unbalanced data} %% without formatting
%% at least one keyword must be supplied

%% publication information
%% NOTE: Typically, this can be left commented and will be filled out by the technical editor
%% \Volume{50}
%% \Issue{9}
%% \Month{June}
%% \Year{2012}
%% \Submitdate{2012-06-04}
%% \Acceptdate{2012-06-04}

%% The address of (at least) one author should be given
%% in the following format:
\Address{
  Johan Lindstr\"om\\
  Mathematical Statistics\\
  Centre for Mathematical Sciences\\
  Lund University\\
  Box 118, SE-221 00 Lund, Sweden\\
  E-mail: \email{johanl@maths.lth.se}\\
  URL: \url{http://www.maths.lth.se/~johanl}
}

%% Note that you should use the \pkg{}, \proglang{} and \code{} commands.
\begin{document}
<<echo=FALSE>>=
##initial set-up: load libraries
library(SpatioTemporal)
library(Matrix)
library(plotrix) 
library(maps)
@ 

\section{Introduction} \label{sec:intro}
\proglang{R} \citep{R2013} package \pkg{SpatioTemporal} provides 
functions for fitting and evaluation of a class of Gaussian spatio-temporal
processes that are based on spatially varying temporal basis functions, with
spatially correlated residuals. 

Development of \pkg{SpatioTemporal} was motivated by the
need, in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air),
for accurate predictions of ambient air pollution. 
MESA Air is a cohort study funded by the Environmental
Protection Agency (EPA) with the aim of assessing the relationship between
chronic exposure to air pollution and the progression of sub-clinical
cardiovascular disease \citep{Kaufman12}. A primary focus of the MESA Air study
is the development of accurate predictions of ambient air pollution
\citep{Bild02,Kaufman12} --- primarily gaseous oxides of nitrogen (\no{x}) and
particulate matter with aerodynamic diameter less than $2.5\ \mu\text{m}$
(\PM{2.5}) --- at the home locations of study participants in six major US
metropolitan areas: Los Angeles, CA; New York, NY; Chicago, IL; 
Minneapolis-St.~Paul, MN; Winston-Salem, NC; and Baltimore, MD.

To fullfill the prediction needs of MESA Air the spatio-temporal model
implemented in this package has been developed in a series of papers
\citep{Szpiro10a, Sampson11, Lindstrom11a, Lindstrom13}. The model is based on
the notion of spatially varying smooth temporal basis functions \citep[see e.g.\
Sec.~3 in][]{Fuentes06}, and represents one of many different ways that
spatio-temporal dependencies can be modelled.

Several general overviews of statistical modeling approaches for spatially and
spatio-temporally correlated data exist \citep{Banerjee04, CressieWikle11},
including non-separable spatio-temporal covariance functions
\citep{GneitingGuttorpHSS10} and dynamic model formulations
\citep{GamermanHSS10}. There are also several methods developed specifically for
the modelling of air pollution data \citep{Smith03, Sahu06, Calder08, Fanshawe08,
  Paciorek09, DeIaco2012}. Additionally, other \proglang{R} packages that handle
spatio-temporal models and data are summarised in the relevant task view
(\url{http://CRAN.R-project.org/view=SpatioTemporal}) on the Comprehensive
\proglang{R} Archive Network (CRAN) \url{http://CRAN.R-project.org}

The main purposes of this paper are to: 1) introduce \pkg{SpatioTemporal}
to \proglang{R} users, 2) present details regarding the model and its
implementation necessary for users to fully understand and analys output from
the package, 3) demonstrate the use of \pkg{SpatioTemporal} by analysing an
example data-set, and 4) provide an outlook of future features that may be added
to the package.

This paper starts with an outline of the model and theory in
Section~\ref{sec:theory}, including details regarding the smooth temporal basis
functions (Section~\ref{sec:temporal_basis}), estimation
(Section~\ref{sec:estimation}), prediction (Section~\ref{sec:prediciton}), and
cross-validation (Section~\ref{sec:CV}). This is followed by a description of
key package features and assumptions in Section~\ref{sec:features} and an example
analysis of Los Angeles \no{x} data in
Section~\ref{sec:example}. Section~\ref{sec:Outlook} concludes with an outlook
towards possible future features.

Results in this paper were obtained using version 1.1.7 of
\pkg{SpatioTemporal}. The current version of the package can be obtained from
CRAN at \url{http://CRAN.R-project.org/package=SpatioTemporal}.

%%TODO change to reference to JSS-paper
A longer vignette providing detailed descriptions of function outputs and
elaborating on features not covered here (e.g.\ simulation) is included as
\code{vignette("ST\_tutorial", package="SpatioTemporal")}.


\section{Model and Theory} \label{sec:theory}
We are interested in models of the form
\begin{equation}
 y(s,t) = \mu(s,t) + \nu(s,t),
 \label{eqn:model_decomp}
\end{equation}
where $y(s,t)$ denotes the spatio-temporal observations, $\mu(s,t)$ is the
structured mean field, and $\nu(s,t)$ is the space-time residual field. 

The mean field is modelled as 
\begin{equation}
 \mu(s,t) = \sum_{l=1}^L \gamma_l \M_l(s,t) + \sum_{i=1}^{m} \beta_i(s) f_i(t),
 \label{eqn:mean_model}
\end{equation}
where the $\M_l(s,t)$ are spatio-temporal covariates; $\gamma_l$ are coefficients
for the spatio-temporal covariates; $\{f_i(t)\}_{i=1}^m$ is a set of (smooth) temporal basis
functions, with $f_1(t)\equiv 1$; and the $\beta_i(s)$ are spatially varying coefficients
for the temporal functions.

The $\beta_i(s)$-coefficients in \eqref{eqn:mean_model} are treated as spatial
fields with a universal kriging structure, allowing the temporal structure to
vary between locations:
\begin{equation}
 \beta_i(s) \in \Normal{X_i \alpha_i}{\Sigma_{\beta_i}(\theta_i)} \quad \text{for }
  i=1,\ldots,m ,
\label{eqn:beta_fields}
\end{equation}
where $X_i$ are $n\,\times\,p_i$ design matrices, $\alpha_i$ are $p_i\,\times\,1$
matrices of regression coefficients, and $\Sigma_{\beta_i}(\theta_i)$ are
$n\,\times\,n$ covariance matrices. The $X_i$ matrices often contain
geographical covariates and we dentote this component a ``land use'' regression
(LUR). This structure allows for different covariates and covariance structures
in the each of the $\beta_i(s)$ fields; the fields are assumed to be apriori
independent of each other.

The residual space-time field, $\nu(s,t)$, is assumed to
be independent in time with stationary, parametric spatial covariance
\begin{equation}
 \nu(s,t) \in \Normal{0}{\underbrace{\begin{bmatrix}
                     \Sigma_\nu ^1(\theta_\nu) & 0 & 0 \\
                     0 & \ddots & 0 \\
                     0 & 0 & \Sigma_\nu^T(\theta_\nu)
                     \end{bmatrix}}_{\Sigma_\nu(\theta_\nu)}},
\label{eqn:nu_fields}
\end{equation}
or
\begin{align*}
\nu(s,t) &\in \Normal{0}{\Sigma_\nu ^t(\theta_\nu)}\ \text{for}\ t=1,\ldots,T &
 &\text{and} &
 \nu(s_1,t_1) &\perp \nu(s_2,t_2),\ t_1 \neq t_2.
\end{align*}
Here the size of each block matrix, $\Sigma_\nu^t(\theta_\nu)$, is the number of
observations, $n_t$, at each time-point. Conceptually the $\nu$-field consists
of a correlated component, $\nu^*$, and an uncorrelated nugget-effect
comprising small-scale variability and measurement errors, $\nu_\text{nugget}$, i.e.:
\begin{align}
  \nu(s,t) &= \nu^*(s,t) + \nu_\text{nugget}(s,t) & 
  &\text{and} &
  \Sigma_\nu &= \Sigma^*_\nu + \Sigma_{\nu,\text{nugget}},
\label{eqn:nu_field_nugget}
\end{align}
where $\Sigma_{\nu,\text{nugget}}$ is a diagonal matrix.

The temporal independence in \eqref{eqn:nu_fields} is based on the assumption
that the temporal basis, $\{f_i(t)\}_{i=1}^m$, in \eqref{eqn:mean_model} accounts
for the temporal correlation in data. A summary of the notation can be found in
Table~\ref{tbl:notation}.

\begin{table}[htb]
\caption{Important notation and symbols}
\begin{tabular}{c|p{9cm}}
Symbol & Meaning \\ \hline
$y(s,t)$ & Spatio-temporal observations. \\
$y_u(s,t)$ & Spatio-temporal process at the un-observed locations/times. \\
$y^*(s,t)$ & Smoothed version of the spatio-temporal process (i.e.\ excl.\ nugget). \\
$z(s,t)$ & The log-Gaussian process, $z(s,t)=\exp(y(s,y))$. \\
$\mu(s,t)$ & Mean field part of $y(s,t)$. \\
$\nu(s,t)$ & Space-time residual part of $y(s,t)$. \\
$f_i(t)$ & Smooth temporal basis functions. \\
$\beta_i(s)$ & Spatially varying regression coefficients, weighing the $i$:th
               temporal basis differently at each location. \\
$X_i$ & Land use regression (LUR) basis functions for the spatially varying
        regression coefficients in $\beta_i(s)$. \\
$\alpha_i$ & Regression coefficients for the $i$:th LUR-basis. \\
$\M_l(s,t)$ & Spatio-temporally varying covariates.\\
$\gamma_l$ & Regression coefficient for the spatio-temporally varying covariates. \\
$N$ & No.\ of observations. \\
$T$ & No.\ of observed time-points. \\
$n$ & No.\ of observed locations. \\
$n_t$ & No.\ of observations at time $t$ ($N=\sum_{t=1}^T n_t$ and $n_t \leq n\ \forall t$. \\
$m$ & No.\ of temporal basis functions (incl.\ intercept).\\
$L$ & No.\ of spatio-temporal model outputs. \\
$p_i$ & No.\ of LUR-basis functions for the $i$:th temporal-basis.
\end{tabular}
\label{tbl:notation}
\end{table}

To simplify the model we introduce a sparse $N\,\times\,mn$-matrix
$F=(f_{st,is^\prime})$ with elements
\begin{align}
f_{st,is^\prime} = \begin{cases}
 f_i(t) & s=s^\prime, \\
 0 & \text{otherwise},
\end{cases}
\label{eqn:F_matrix}
\end{align}
along with the $N\,\times\,1$-vectors $Y=y(s,t)$, $V=\nu(s,t)$, and
$\M_l(s,t)$ by stacking the elements into single vectors varying first $s$
and then $t$, i.e.\ (assuming that the corresponding observations exist)
\begin{align*}
Y = \begin{bmatrix} y(s_1,1) & y(s_2,1) & \cdots & y(s_1,2) & y(s_2,2) & \cdots
  & y(s_n,T)
\end{bmatrix}^\trsp.
\end{align*}
The unknown regression and covariance parameters of the model are are collected
into column vectors
\begin{align*}
\mv{\gamma} &= \begin{bmatrix} \gamma_1 & \cdots & \gamma_L \end{bmatrix}^\trsp, &
\mv{\alpha} &= \begin{bmatrix} \alpha_1^\trsp & \cdots &
  \alpha_m^\trsp \end{bmatrix}^\trsp, &
\theta_B &= \{\theta_i\}_{i=1}^m, &
\Psi &= \{\theta_B, \theta_\nu\},
\end{align*}
and all spatio-temporal covariates are gathered in a $N\,\times\,L$-matrix,
$\M = \begin{bmatrix} \M_1 & \cdots & \M_L \end{bmatrix}$. Components of
the $\beta_i$-fields are assembled into block matrices as
\begin{align}
B &= \begin{bmatrix}\beta_1(s) \\ \vdots \\ \beta_m(s) \end{bmatrix}, &
X &= \begin{bmatrix}
      X_1 &    0   & 0 \\
        0 & \ddots & 0 \\
        0 &    0   & X_m
     \end{bmatrix},
&
\Sigma_B(\theta_B) &= \begin{bmatrix}
                     \Sigma_1 (\theta_1) & 0 & 0 \\
                     0 & \ddots & 0 \\
                     0 & 0 & \Sigma_m(\theta_m)
                     \end{bmatrix}.
\label{eqn:block_matrix}
\end{align}
Using these matrices \eqref{eqn:model_decomp} can be written as
\begin{align}
 Y &=  \M \mv{\gamma} + FB + V, &
 &\text{where} &
B &\in \Normal{X \mv{\alpha}}{\Sigma_B(\theta_B)} &
 &\text{and} &
V &\in \Normal{0}{\Sigma_\nu(\theta_\nu)}.
\label{eqn:model_matrix}
\end{align}
Since \eqref{eqn:model_matrix} is a linear combinations of independent
Gaussians we introduce the matrices
\begin{align}
\widetilde{X} &= \begin{bmatrix} \M & FX \end{bmatrix} &
 &\text{and} &
\widetilde{\Sigma}(\Psi) &= \Sigma_\nu(\theta_\nu) + F \Sigma_B(\theta_B) F ^\trsp,
\label{eqn:tilde_matrices}
\end{align}
and write the distribution of $Y$ as
\begin{equation}
[ Y \vert \Psi,\mv{\gamma},\mv{\alpha} ] \in
  \Normal{\widetilde{X} \begin{bmatrix}\mv{\gamma} \\ \mv{\alpha} \end{bmatrix}}
         {\widetilde{\Sigma}(\Psi)}.
\label{eqn:distr_Y}
\end{equation}
Having defined the model the following Sections discuss:
\ref{sec:temporal_basis}) specification of the (smooth) temporal basis functions,
\ref{sec:estimation}) parameter estimation,
\ref{sec:prediciton}) prediction, and
\ref{sec:CV}) model evaluation through cross-validation. 


\subsection{Smooth Temporal Functions} \label{sec:temporal_basis}
The objective of the smooth temporal basis functions, $f_i(t)$, is to capture
the temporal variability in the data. These functions can either be specified as
{\em deterministic functions}, or obtained as {\em smoothed singular vectors}
\citep[See][for details.]{Fuentes06}.

To derive the $m-1$ smoothed singular vectors ($m-1$ since $f_1(t)\equiv 1$) we
first construct the $T\,\times\,n$ data matrix 
\begin{align}
  D(t,s) = \begin{cases} 
    y(t,s), & \text{if the observation } y(t,s) \text{ exists}, \\
    \text{NA}, & \text{otherwise,}
  \end{cases}
  \label{eqn:data_matrix}
\end{align}
and fill in missing observations using the algorithm described by
\citet{Fuentes06}:
\begin{enumerate}
\item[Step 0] Centre and scale each column (to mean zero, variance one) and
  compute the mean of all available observations for each time-point,
  $u_1(t)$. Missing values in $D(t,s)$ are then imputed using fitted values from a
  linear regression where each column of $D(t,s)$ is regressed onto $u_1$. For this
  step to be well defined the data matrix must have {\em at least one}
  observation in each {\em row and column}.
\item[Step 1.] Compute the SVD (singular value decomposition) of the new data
  matrix with the missing values imputed. 
\item[Step 2.] Do regression of each column of the new data matrix on the first
  $m-1$ orthogonal basis functions from Step 1. The  missing values are then
  replaced by the fitted values of this regression. 
\item[Step 3.] Repeat from Step 1 until convergence; convergence being 
  measured by the change in the imputed values between iterations.
\end{enumerate}   
Having imputed the missing values in $D(t,s)$ we then use splines to smooth
the leading $m-1$ singular vectors, i.e. the $m-1$ first columns of $U$ in the SVD:
$D = U S V^\trsp$.

Cross-validation can be used to determine the number of smooth temporal basis
functions needed to capture the temporal variability in data. In a
cross-validation the $j^\text{th}$ column of $D(t,s)$ is held out, smooth
temporal functions are computed for the reduced matrix as above, and the
functions are evaluated by how well they explain the held out $j^\text{th}$
column of $D(t,s)$. Repeating for all columns in $D(t,s)$ we obtain a set of
regression statistics describing how well the left out columns are explained by
smooth temporal functions based on the remaining columns. The computed
statistics --- mean squared errors (MSE), $R^2$, AIC 
(Akaike information criterion), and BIC (Bayesian information criterion) --
together with correlation analysis of temporal residuals (recall the assumption
of temporal independence in \eqref{eqn:nu_fields}) are used to determine a
suitable number of temporal basis functions; an example is provided in
Section~\ref{sec:Ex_trends}.


\subsection{Parameter Estimation} \label{sec:estimation}
Parameter estimates are obtained by maximising the log-likelihood of
\eqref{eqn:distr_Y}
\begin{equation}
\begin{split}
  2 l(\Psi,\alpha,\gamma \vert Y) =&
  - N\log(2\pi) - \log\det{\widetilde{\Sigma}(\Psi)}
  \\ &
 - \left(Y - \widetilde{X} \begin{bmatrix}\mv{\gamma}
   \\ \mv{\alpha} \end{bmatrix} \right)^\trsp \widetilde{\Sigma}^{-1}(\Psi)
   \left(Y - \widetilde{X} \begin{bmatrix}\mv{\gamma}
   \\ \mv{\alpha} \end{bmatrix} \right).
\end{split}
\label{eqn:full_likelihood}
\end{equation}
Here $\widetilde{\Sigma}$ is a dense $N\,\times\,N$-matrix and to obtain a
computationaly feasable solution that utlizes the block diagonal structure of
$\Sigma_\nu$ and $\Sigma_B$ \citet{Lindstrom11a,Lindstrom13} simplified the
log-likelihood by application of various matrix identities, including the
Woodbury identity \citep[Thm.~18.2.8][]{Harville97} 
\begin{equation}
 \widetilde{\Sigma}^{-1} = \Sigma_\nu^{-1} - \Sigma_\nu^{-1} F 
    \left(\Sigma_B^{-1} + F^\trsp \Sigma_\nu^{-1} F\right)^{-1}
     F^\trsp \Sigma_\nu^{-1},
\label{eqn:sigma_nu_woodbury}
\end{equation}
and replaced $\mv{\gamma},\ \mv{\alpha}$ with the generalised least squares
estimates
\begin{equation}
  \begin{bmatrix} \widehat{\mv{\gamma}} \\ \widehat{\mv{\alpha}} \end{bmatrix} =
  \left( \widetilde{X}^\trsp \widetilde{\Sigma}^{-1} \widetilde{X}\right)^{-1}
  \left( \widetilde{X}^\trsp \widetilde{\Sigma}^{-1} Y \right).
  \label{eqn:alpha_gamma}
\end{equation}

Introducing the matrices
\begin{subequations}
\label{eqn:matrix_defns}
\begin{align}
 \Sigma_{B\vert Y}^{-1} =& \Sigma_B^{-1} + F^\trsp \Sigma_\nu^{-1} F,
\label{eqn:sigma_B_C}
\\
 \Sigma_{\alpha\vert Y}^{-1} =& X^\trsp \Sigma_B^{-1} X -
    X^\trsp \Sigma_B^{-1} \Sigma_{B\vert Y} \Sigma_B^{-1} X,
\label{eqn:sigma_alpha_C}
\\
\begin{split}
 \widehat{\Sigma} =& \Sigma_\nu^{-1} - \Sigma_\nu^{-1} F \Sigma_{B\vert Y} F^\trsp \Sigma_\nu^{-1}
\\&
- \Sigma_\nu^{-1} F \Sigma_{B\vert Y} \Sigma_B^{-1} X \Sigma_{\alpha\vert Y}
   X^\trsp \Sigma_B^{-1} \Sigma_{B\vert Y} F^\trsp \Sigma_\nu^{-1},
\end{split}
\label{eqn:sigma_hat}
\end{align}
\end{subequations}
and using \eqref{eqn:alpha_gamma} the log-likelihood \eqref{eqn:full_likelihood}
can be replaced by the corresponding profile or restricted maximum
log-likelihood (REML):
\begin{equation}
\begin{split}
2 l_\text{\sc prof}(\Psi \vert Y) =&
 - \log\det{\Sigma_\nu(\theta_\nu)}
 - \log\det{\Sigma_B(\theta_B)}
 - \log\det{\Sigma_{B\vert Y}^{-1}(\Psi)}
 - Y^\trsp \widehat{\Sigma}(\Psi) Y
\\&
 + Y^\trsp \widehat{\Sigma}(\Psi) \M
   \Bigl( \M^\trsp \widehat{\Sigma}(\Psi) \M \Bigr)^{-1}
   \M^\trsp \widehat{\Sigma}(\Psi) Y + \text{const.}
\end{split}
\label{eqn:profile_likelihood}
\end{equation}
or
\begin{equation}
2 l_\text{\sc reml}(\Psi \vert Y) = 2 l_\text{\sc prof}(\Psi \vert Y)
-\log\det{\M^\trsp \widehat{\Sigma}(\Psi) \M} -\log\det{\Sigma_{\alpha\vert Y}^{-1}(\Psi)}
\label{eqn:REML_likelihood}
\end{equation}
respectively. Parameter estimates are obtained by maximisation of either
\eqref{eqn:profile_likelihood} or \eqref{eqn:REML_likelihood} as
\begin{align}
 \widehat{\Psi}_\text{\sc prof} &= \argmax{\Psi} l_\text{\sc prof}(\Psi \vert Y),
 & &\text{or} &
 \widehat{\Psi}_\text{\sc reml} &= \argmax{\Psi} l_\text{\sc reml}(\Psi \vert Y).
\label{eqn:parameter_estimation}
\end{align}


\subsection{Predictions} \label{sec:prediciton}
Given the structure of the model \eqref{eqn:model_decomp} with a mean component
\eqref{eqn:mean_model} and $\beta$-fields \eqref{eqn:beta_fields} the
contribution to any predictions of unobserved $Y$'s can be decomposed in to
parts due to the regression model: $\M\gamma+FX\alpha$, the $\beta$-fields:
$\M\gamma + FB$, and the full predictions. These different predictions are 
illustrated in Figure~\ref{fig:predict_example}, using data from the example in
Section~\ref{sec:example}. The different predicitons play an important part in
model evaluation by highlighting at which level of the model different featurs
of the data are captured.

First, given observations, $Y$, and estimates of the covariance parameters, $\Psi$,
the regression coefficients are given by \eqref{eqn:alpha_gamma} with variances 
\begin{equation*}
  \VAR\left( \begin{bmatrix} \widehat{\mv{\gamma}}
    \\ \widehat{\mv{\alpha}} \end{bmatrix} \middle\vert Y, \Psi \right) =
  \left( \widetilde{X}^\trsp \widetilde{\Sigma}^{-1} \widetilde{X}\right)^{-1}.
\end{equation*}

Before providing predictions for $\beta$ and $Y$ some notation is needed.
Let $\M_u$, $X_u$ and $F_u$ denote spatio-temporal covariates, geographic
covariates, and temporal basis funcions at unobserved locations/times. Further,
$B_u$ denotes the collection of $\beta$-fields at the unobserved locations,
$\Sigma_{B,uo}$ and $\Sigma_{\nu,uo}$ are the cross-covariance matrices between
observed and unobserved points, and $\Sigma_{B,uu}$ and $\Sigma_{\nu,uu}$ are
the covariance matrices for unobserved points. Using this notation relevant
variations on the matrices in \eqref{eqn:tilde_matrices} are
\begin{align}
\widetilde{X}_u &= \begin{bmatrix} \M_u & F_u X_u \end{bmatrix} &
 &\text{and} &
\widetilde{\Sigma}_{uo} &= \Sigma_{\nu,uo} + F_u \Sigma_{B,uo} F ^\trsp.
\label{eqn:tilde_matrices_unobs}
\end{align}


\subsubsection[Prediction of beta-fields]{Prediction of $\beta$-fields}
Treating \eqref{eqn:model_matrix} as a hierarchical model straight forward but
tedious calculations give predictions for the $\beta$-fields as
\begin{equation}
  \E\left( B_u  \middle\vert Y, \Psi \right) =
   X_u \widehat{\mv{\alpha}} + \Sigma_{B,uo} F^\trsp \widetilde{\Sigma}^{-1}
  \left(Y - \widetilde{X} \begin{bmatrix} \widehat{\mv{\gamma}}
    \\ \widehat{\mv{\alpha}} \end{bmatrix} \right),
  \label{eqn:EX_beta}
\end{equation}
with variance
\begin{equation}
\begin{split}
  \VAR\left( B_u \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma} \right) =&
  \Sigma_{B,uu} - \Sigma_{B,uo} \Sigma_B^{-1} \Sigma_{B,ou} +
  \Sigma_{B,uo} \Sigma_B^{-1} \Sigma_{B\vert Y} \Sigma_B^{-1} \Sigma_{B,ou}
 \\=&                 
  \Sigma_{B,uu} - \Sigma_{B,uo} F^\trsp \Sigma_\nu^{-1} F \Sigma_{B\vert Y}
                 \Sigma_B^{-1} \Sigma_{B,ou}.
\end{split}
  \label{eqn:VX_beta}
\end{equation}
Here, the first two terms in the top line of \eqref{eqn:VX_beta} contain the
standard spatial prediction uncertainty for $\beta$, the last term is the
added uncertainty from estimating $\beta$ at the observed locations given $Y$.
The variance in \eqref{eqn:VX_beta} is conditional on both regression and
covariance parameters, adding the uncertainty in the regression
coefficients the variance becomes
\begin{equation}
\begin{split}
  \VAR\left( B_u \middle\vert Y, \Psi \right) =&
  \VAR\left( B_u \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma} \right) +
  \left(\begin{bmatrix} 0 & X_u \end{bmatrix} - \Sigma_{B,uo} F^\trsp
        \widetilde{\Sigma}^{-1} \widetilde{X} \right) \cdot
  \\&
  \left( \widetilde{X}^\trsp \widetilde{\Sigma}^{-1} \widetilde{X}\right)^{-1}
  \left(\begin{bmatrix} 0 & X_u \end{bmatrix} - \Sigma_{B,uo} F^\trsp
        \widetilde{\Sigma}^{-1} \widetilde{X} \right)^\trsp.
\end{split}
  \label{eqn:VX_REML_beta}
\end{equation}
For $\beta$-fields at observed locations \eqref{eqn:EX_beta} and
\eqref{eqn:VX_beta} simplify to
\begin{align*}
  \E\left( B \middle\vert Y, \Psi \right)
  =& X \widehat{\mv{\alpha}} + \Sigma_{B\vert Y} F^\trsp \Sigma_\nu^{-1}
  \left(Y - \widetilde{X} \begin{bmatrix} \widehat{\mv{\gamma}}
    \\ \widehat{\mv{\alpha}} \end{bmatrix} \right),
\\
  \VAR\left( B \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma} \right) =&
    \Sigma_{B\vert Y}.
\end{align*}


\subsubsection{Prediction of Y}
The model \eqref{eqn:distr_Y} is multivariate Normal and the full predictions of
unobserved Y's are in principal standard kriging estimates. For the
predictions we are primarily interested in the smooth underlying field, denoted 
$Y^*$; this can also been interpreted as smoothing over the nugget in
\eqref{eqn:nu_field_nugget}  \citep[Ch.~3.2.1]{Cressie93}.
The predictions of $Y^*$ and $Y$ \emph{differ only at observed locations}.

Using \eqref{eqn:tilde_matrices} and \eqref{eqn:tilde_matrices_unobs} 
predictions for $Y^*_u$ are
\begin{equation}
  \begin{split}
  \E\left( Y^*_u \middle\vert Y, \Psi \right) &= 
  \widetilde{X}_u \begin{bmatrix} \widehat{\mv{\gamma}} \\
    \widehat{\mv{\alpha}} \end{bmatrix} + 
  \widetilde{\Sigma}^*_{uo} \widetilde{\Sigma}^{-1} 
  \left(Y - \widetilde{X} 
    \begin{bmatrix} \widehat{\mv{\gamma}} \\
      \widehat{\mv{\alpha}} \end{bmatrix} \right),
  \end{split}
  \label{eqn:EX}
\end{equation}
with variances
\begin{subequations}
  \label{eqn:VX_Y}
  \begin{align}
    \VAR\left( Y^*_u \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma} \right) =&
    \widetilde{\Sigma}^*_{uu} - \widetilde{\Sigma}^*_{uo}
    \widetilde{\Sigma}^{-1} \widetilde{\Sigma}^*_{ou} 
    \label{eqn:VX}
    \\
    \VAR\left( Y_u \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma} \right) =&
    \widetilde{\Sigma}_{uu} - \widetilde{\Sigma}^*_{uo}
    \widetilde{\Sigma}^{-1} \widetilde{\Sigma}^*_{ou}
    \label{eqn:VX_pred}
  \end{align}
\end{subequations}
Here $\widetilde{\Sigma}^*_{uo}$ is the cross-covariance matrix {\em excluding} 
the nugget in $\nu$, cf.~\eqref{eqn:nu_field_nugget}; this distinction is only
relevant when $Y^*_u$ includes observed points. For the prediction variances,
\eqref{eqn:VX} gives the uncertainty in the prediction of the underlying
$y^*$-field, while \eqref{eqn:VX_pred} gives the uncertainty for a new
observations at this point; the difference is similar to that between
confidence and prediction intervals in regression \citep[Ch.~11.3.5
in][]{CasellaBerger02} and is of importance for the cross-validation.
As for \eqref{eqn:VX_beta},  \eqref{eqn:VX_Y} is conditional on both regression
and covariance parameters, accounting for uncertainty in the regression
coefficients gives
\begin{equation}
  \begin{split}
    \VAR\left( Y^*_u \middle\vert Y, \Psi\right) =&
    \VAR\left( Y^*_u \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma} \right) +
    \\ &
    \left(\widetilde{X}_u -  \widetilde{\Sigma}^*_{uo} \widetilde{\Sigma}^{-1}
      \widetilde{X} \right)
    \left( \widetilde{X}^\trsp \widetilde{\Sigma}^{-1} \widetilde{X}\right)^{-1}
    \left(\widetilde{X}_u -  \widetilde{\Sigma}^*_{uo} \widetilde{\Sigma}^{-1}
      \widetilde{X} \right)^\trsp
  \end{split}
  \label{eqn:VX_REML_Y}
\end{equation}
for \eqref{eqn:VX} and similarly for \eqref{eqn:VX_pred}.


For unobserved time-points it should be noted that the lack of temporal
correlation in $\nu$ implies that predictions of $y(s,t_u)$ are identical to the
contribution from the $\beta$-fields in \eqref{eqn:EX_beta} (see
Figure~\ref{fig:predict_example}), 
\begin{align*}
  \E\left(y(s,t_u) \middle\vert Y, \Psi \right) =
  \M_u \widehat{\mv{\gamma}} + F_u \E\left(B_u \middle\vert Y, \Psi \right).
\end{align*}
The prediciton variance for unobserved time-points,
\begin{align*}
  \VAR\left(y(s,t_u) \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma} \right) =
  F_u\, \VAR\left(B_u \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma} \right) 
  F_u^\trsp + \Sigma_{\nu,uu},
\end{align*}
will typically be much larger than for observed time-points due to the
added uncertainty from the completely unknown $\nu(s,t_u)$-field.

<<eval=TRUE, echo=FALSE>>=
##load precomputed results
data(mesa.data.raw, package="SpatioTemporal")
data(mesa.model, package="SpatioTemporal")
data(est.mesa.model, package="SpatioTemporal")
  
##create model without spatiotemporal covariate 
##(needed to be able to add time-points)
mesa.data <- createSTdata(mesa.data.raw$obs, mesa.data.raw$X, n.basis=2,
                          SpatioTemporal=mesa.data.raw["lax.conc.1500"])
mesa.model <- createSTmodel(mesa.data, LUR=mesa.model$LUR.list, 
                            ST=NULL, cov.beta=mesa.model$cov.beta, 
                            cov.nu=mesa.model$cov.nu,
                            locations=mesa.model$locations.list)

##restrict mesa.data to one location
mesa.model.obs <- dropObservations(mesa.model,
                                   mesa.model$obs$ID == "60590007")
  
##restrict mesa.data to one location
mesa.data$covars <- mesa.data$covars[mesa.data$covars$ID=="60590007",,drop=FALSE]
mesa.data$obs <- mesa.data$obs[mesa.data$obs$ID=="60590007",,drop=FALSE]
mesa.data$SpatioTemporal <- NULL
##add more temporal points (every 3.5 days)
mesa.data.2 <- updateTrend(mesa.data, fnc=mesa.data$trend.fnc,
                           extra.dates=seq(min(mesa.data$trend$date),
                             max(mesa.data$trend$date), by=3.5))
  
##nugget (using first value is fine; it is an AQS-site)
pars <- coef(est.mesa.model, pars="cov")$par
nugget <- loglikeSTgetPars(pars, mesa.model)$cov.nu$nugget[1]
  
##predictions at this location (every 14 days)
pred <- predict(mesa.model.obs, pars, mesa.data,
                nugget.unobs=nugget)
##new predictions
pred.2 <- predict(mesa.model.obs, pars,  mesa.data.2,
                  nugget.unobs=nugget)

##predictions for log-field
pred.log <- predict(mesa.model.obs, pars, mesa.data,
                    nugget.unobs=nugget, transform="unbiased", 
                    type="p")
pred.log.2 <- predict(mesa.model.obs, pars, mesa.data.2,
                      nugget.unobs=nugget, transform="unbiased", 
                      type="p")
@ 
<<label=figPredictExample, eval=FALSE, echo=FALSE>>=
##prediction interval for additional time-points
plot(pred.2, ID="60590007", main="Predictions at AQS 60590007",
     xlab="", ylab="NOx (log pbb)", xaxt="n",
     xlim=as.Date(c("2006-07-01","2007-01-01")),
     ylim=c(2.3,5.2), pch=NA, pred.var=TRUE,
     lty=NA, col=c(1,1,"lightgrey"))
##prediction interval for original timepoints
plot(pred, ID="60590007", pred.var=TRUE, add=TRUE, 
     lty=NA, pch=NA, col=c(1,1,"darkgrey"))
##confidence interval for original timepoints
plot(pred, ID="60590007", add=TRUE, lty=NA, pch=NA, col=c(1,1,"white"))
##predictions for additional time-points, due to mean component
plot(pred.2, ID="60590007", add=TRUE, pred.type = "EX.mu", 
     lty=c(4,NA), lwd=2, pch=NA, col=c(1,NA,NA))
##predictions for additional time-points
plot(pred.2, ID="60590007", add=TRUE, pred.type = "EX", 
     lty=c(1,NA), lwd=2, pch=NA, col=c(1,NA,NA))
##predictions for additional time-points, due to beta-field
plot(pred.2, ID="60590007", add=TRUE, pred.type = "EX.mu.beta", 
     lty=c(2,NA), lwd=2, pch=NA, col=c("green",NA,NA))
##observations and predictions at original time-scale
plot(pred, ID="60590007", STmodel=mesa.data.2, add=TRUE, 
     pred.type = "EX", lty=NA, pch=c(3,4), col=c(1,2,NA))
@ 

\subsubsection{Temporal averages}
A primary interest in MESA Air is the health effects of chronic exposure to air
pollution. Thus we are interested in the long term average exposure at each location,
$\overline{y}(s) = \left( \sum_t y(s,t) \right) / T$. Predictions and
uncertainties of temporal averages are given by
\begin{subequations}
  \label{eqn:LTA}
  \begin{align}
  \E\left( \overline{y^*}(s) \middle\vert Y, \Psi\right) &=
  \frac{1}{T} \sum_{t=1}^T \E\left( y^*(s,t) \middle\vert Y, \Psi\right),
    \label{eqn:EX_LTA}
  \\
  \VAR\left( \overline{y^*}(s) \middle\vert Y, \Psi\right) &=
  \frac{1}{T^2} \sum_{t_1=1}^T \sum_{t_2=1}^T 
  \COV\left( y^*(s,t_1), y^*(s,t_2) \middle\vert Y, \Psi\right),
    \label{eqn:VX_LTA}
  \end{align}
\end{subequations}
where $\COV\left( y^*(s,t_1), y^*(s,t_2) \middle\vert Y, \Psi\right)$ is the
matrix form of \eqref{eqn:VX_REML_Y}.

\subsubsection[log-Gaussian fields]{$\log$-Gaussian fields}
Transformation of data is commonly used to facilitate the modelling of
non-Gaussian data under Gaussian assumptions \citep{Tukey57,BoxCox64}.
The $\log$-transformation has been successfully applied to environmental data,
including, but not limited to, air-pollution \citep[e.g.\ \PM{10}, \PM{2.5} and
\no{x}; see][]{Paciorek09,Szpiro10a,Sampson11} and precipitation \citep{Damian03}.
For $\log$-transformed data exact expressions exist for both bias-corrected
estimates and their associated mean squared prediction errors (MSPE) 
\citep{Cressie93, Cressie06, DeOliveira06}. This stands in contrast to the
approximate $\delta$-method described in Ch.~3.2.2 of \citet{Cressie93} for
general trans-Gaussian Kriging.

To accomondate $\log$-transformed data both point and temporal average
predictions for $\log$-Gaussian processes, as described below, are implemented
in \pkg{SpatioTemporal}. Figure~\ref{fig:predict_example} illustrates the
difference between predictions of the $\log$-Gaussian process (original data)
and of the Gaussian process (transformed data).

<<label=figPredictLogExample, eval=FALSE, echo=FALSE>>=
##prediction interval for additional time-points
plot(pred.log.2, pred.type="EX.pred", ID="60590007", 
     main="",  xlab="", ylab="NOx (pbb)", 
     xlim=as.Date(c("2006-07-01","2007-01-01")),
     ylim=c(0,185), pred.var=TRUE, pch=NA, 
     lty=NA, col=c(1,1,"lightgrey"))
##prediction interval for original timepoints
plot(pred.log, pred.type="EX.pred", ID="60590007", pred.var=TRUE, add=TRUE,
     lty=NA, pch=NA, col=c(1,1,"darkgrey"))
##confidence interval for original timepoints
plot(pred.log, ID="60590007", add=TRUE, 
     lty=NA, pch=NA, col=c(1,1,"white"))
##predictions for additional time-points, due to mean component
plot(pred.log.2, ID="60590007", add=TRUE, pred.type = "EX.mu", 
     lty=c(4,NA), lwd=2, pch=NA, col=c(1,NA,NA))
##predictions for additional time-points
plot(pred.log.2, ID="60590007", add=TRUE, pred.type = "EX.pred", 
     lty=c(1,NA), pch=NA, col=c("orange",NA,NA), lwd=3, pred.var=TRUE)
plot(pred.log.2, ID="60590007", add=TRUE, pred.type = "EX", 
     lty=c(1,NA), pch=NA, col=c(1,NA,NA), lwd=2)
##predictions for additional time-points, due to beta-field
plot(pred.log.2, ID="60590007", add=TRUE, pred.type = "EX.mu.beta", 
     lty=c(2,NA), lwd=2, pch=NA, col=c("green",NA,NA))
##observations and predictions at original time-scale
plot(pred.log, ID="60590007", STmodel=mesa.data.2, add=TRUE, 
     pred.type = "EX", lty=NA, pch=c(3,4), col=c(1,2,NA))
legend("topleft", c("Observations", 
                    "Predictions",
                    "Predictions, incl. nugget",
                    "Contribution from beta",
                    "Contribution from mean",
                    "95% CI"), bty="n",
       lty=c(NA,1,1,2,4,NA), lwd=c(NA,2,2,2,2,NA),
       pch=c(4,3,NA,NA,NA,15), pt.cex=c(1,1,NA,NA,NA,2.5),
       col=c("red", 1, "orange", "green", 1, "grey"))
@
%$
\begin{figure}[!thb]
\centering
<<fig=TRUE, echo=FALSE, height=5>>=
par(mfcol=c(2,1), mar=c(.5,3.3,2,1), mgp=c(2,1,0))
<<figPredictExample>>
par(mar=c(2.3,3.3,0,1))
<<figPredictLogExample>>
@
\caption{Example of predictions on the Gaussian (top) and on the original scale
  (bottom) at an AQS site (60590007) given data at all other locations.
  The black line gives the predictions ($\E\left(Y^*_u \middle\vert Y \right)$
  or $\Zhat{u}$); the orange line is $\widehat{Z_u}$, i.e.\ predictions incl.\
  the nugget (lower pane only); the dashed green line gives the contribution
  from the $\beta$-fields, $\M\widehat{\mv{\gamma}} + F \E\left(B_u
      \middle\vert Y \right)$; and the dotted line is the contribution
  from the mean, $\M\widehat{\mv{\gamma}} + FX\widehat{\mv{\alpha}}$. For the
  lower pane, the last two are simply transformed as $\exp\left[ \cdot \right]$
  without bias correction.
  Observations occur every 14-days (red $\times$), predictions at these
  time-points (black $+$) are close to the observations while observations at
  un-observed time-points co-incide with (top), or are close to (bottom), the
  dashed green line. Three different $95\%$ intervals are given: a confidence
  interval for observed time-points (white), prediction interval for observed
  time-points (dark grey), and prediction interval for the additional
  un-observed time-points (light grey).}
\label{fig:predict_example}
\end{figure}

If $y(s,t)$ is a Gaussian random process \eqref{eqn:distr_Y} then the
corresponding $\log$-Gaussian random process (i.e.\ the original, untransformed,
data) is given by $z(s,t) = \exp\left( y(s,t) \right)$ with expectation
\begin{align*}
  \mu_z(s,t) &= \E\left( z(s,t) \right) = 
  \exp\left( \E\left( y(s,t) \right) +  \frac{\VAR\left( y(s,t) \right)}{2} \right).
\end{align*}

Assuming that both covariance ($\Psi$) and regression ($\mv{\alpha},
\mv{\gamma}$) parameters are known the best unbiased predictor of an unobserved
part of the $\log$-Gaussian field is \citep[Ch.~3.2.2]{Cressie93}
\begin{align}
  \Zhat{u} &= \exp\left( \E\left( Y^*_u \middle\vert Y, \Psi \right)
    + \frac{\VAR\left( Y^*_u \middle\vert Y, \Psi, \mv{\alpha}, 
        \mv{\gamma} \right)}{2} \right).
  \label{eqn:EZ}
\end{align}
Here we are, just as in \eqref{eqn:EX}, interested in the underlying smooth
field excluding the nugget \citep[see Appendix A in][]{Cressie06}; to obtain a
predictor, $\widehat{Z_u}$, that includes the nugget, the variance from
\eqref{eqn:VX} is replaced by the variance from \eqref{eqn:VX_pred} in
\eqref{eqn:EZ}. The MSPE of $\Zhat{u}$ is,
\begin{equation}
  \begin{split}
  \MSPE\left( \Zhat{u} \right) &= \mu_z(s_u,t_u)^2 \left( 
    \exp\left( \widetilde{\Sigma}^*_{uu} \right) -
    \exp\left( \widetilde{\Sigma}^*_{uo} \widetilde{\Sigma}^{-1}
      \widetilde{\Sigma}^*_{ou} \right)
    \right)
    \\ &\approx \Zhat{u}^2 
    \exp\left( \widetilde{\Sigma}^*_{uu} \right)
    \biggl[ 1 -
    \exp\Bigl( -\VAR\left( Y^*_u \middle\vert Y, \Psi, \mv{\alpha}, 
        \mv{\gamma} \right) \Bigr)
    \biggr],
  \end{split}
  \label{eqn:MSPE}
\end{equation}
where the approximation follows since $\Zhat{u}$ is an unbiased estimator of
$\mu_z(s,t)$. Prediction and confidence intervals for $z(s,t)$ are obtained by
transformation of the corresponding intervals obtained for $y(s,t)$.

The distinction between predictions with and without nugget is much more
important for the $\log$-Gaussian case \eqref{eqn:EZ} than for the Gaussian
case \eqref{eqn:EX}. In general $\Zhat{u} \neq \widehat{Z_u}$, since
$$
\VAR\left( Y^*_u \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma}\right) \neq 
\VAR\left( Y_u \middle\vert Y, \Psi, \mv{\alpha}, \mv{\gamma}\right),\ 
\text{whereas}\ 
\E\left( Y^*_u \middle\vert Y, \Psi \right) \neq \E\left( Y_u \middle\vert Y,
  \Psi \right)
$$
only at observed locations. The difference between
$\Zhat{u}$ and $\widehat{Z_u}$ is important when comparing predictions to
left-out observations in cross-validation.

For the case with unknown regression parameters two possible predictors exist,
either the unbiased predictor $\Zhathat{ub}{u}$ \citep{DeOliveira06,Cressie06}
or the biased, minimum mean squared error predictor $\Zhathat{me}{u}$
\citep{DeOliveira06},
\begin{subequations}
  \label{eqn:EZ_REML}
  \begin{align}
    \Zhathat{ub}{u} &= \exp\left( \E\left( Y^*_u \middle\vert Y, \Psi \right)
      + \frac{\VAR\left( Y^*_u \middle\vert Y, \Psi \right)}{2}
      - \Lambda_z \right),
    \label{eqn:EZ_REML_UB}
    \\
    \Zhathat{me}{u} &= \exp\left( \E\left( Y^*_u \middle\vert Y, \Psi \right)
      + \frac{\VAR\left( Y^*_u \middle\vert Y, \Psi \right)}{2} 
      - 2 \Lambda_z \right).
    \label{eqn:EZ_REML_ME}
  \end{align}
\end{subequations}
Here $\Lambda_z$ is the Lagrange multiplier for the Kriging predictor in
\eqref{eqn:EX},
\begin{align}
  \Lambda_z = \left(\widetilde{X}_u -  \widetilde{\Sigma}^*_{uo}
      \widetilde{\Sigma}^{-1} \widetilde{X} \right)
    \left( \widetilde{X}^\trsp \widetilde{\Sigma}^{-1} \widetilde{X}\right)^{-1}
    \widetilde{X}_u^\trsp.
    \label{eqn:Lambda}
\end{align}
The MSPE of the predictors in \eqref{eqn:EZ_REML} are
\begin{subequations}
  \label{eqn:MSPE_REML}
  \begin{align}
    \MSPE\left( \Zhathat{ub}{u} \right) &\approx \left( \Zhathat{ub}{u} \right)^2
    \exp\left( \widetilde{\Sigma}^*_{uu} \right)
    \biggl[ 1 - \exp\Bigl( -\VAR\left( Y^*_u \middle\vert Y, \Psi\right) \Bigr)
    \Bigl( 2 e^{\Lambda_z} - e^{2 \Lambda_z} \Bigr) \biggr],
    \label{eqn:MSPE_REML_UB}
    \\  
    \MSPE\left( \Zhathat{me}{u} \right) &\approx \left( \Zhathat{ub}{u} \right)^2
    \exp\left( \widetilde{\Sigma}^*_{uu} \right)
    \biggl[ 1 - \exp\Bigl( -\VAR\left( Y^*_u \middle\vert Y, \Psi \right) \Bigr)
    \biggr],
    \label{eqn:MSPE_REML_ME}
  \end{align}
\end{subequations}
where we have used that $\Zhathat{ub}{u}$ is an unbiased estimator of
$\mu_z(s,t)$. We note that
$$
\MSPE\left( \Zhathat{me}{u} \right) \leq \MSPE\left( \Zhathat{ub}{u} \right)
$$
in accordance with \citet{DeOliveira06}

Following the discussion of block prediction for log-Gaussian processes in
\citet{DeOliveira06} and \citet{Cressie06}, estimates of temporal averages are
computed as 
\begin{align}
  \zhatLTA =& \frac{1}{T} \sum_{t=1}^T \zhat{u}(s,t),
  \label{eqn:log_LTA}
\end{align}
and similarly for $\zhathatLTA{ub}$ and $\zhathatLTA{me}$. The MSPE for these
predictors are
\begin{subequations}
  \label{eqn:log_LTA_MSPE}
  \begin{align}
    \begin{split}
      \MSPE\left( \zhatLTA \right) \approx& \frac{1}{T^2} \sum_{t_1=1}^T \sum_{t_2=1}^T
      \zhat{}(s,t_1) \zhat{}(s,t_2) 
      \\ &
      \left[ 
    \exp\left( \begin{bmatrix} \widetilde{\Sigma}^*_{uu} 
    \end{bmatrix}_{st_1,st_2} \right) -
    \exp\left( \begin{bmatrix} \widetilde{\Sigma}^*_{uo} \widetilde{\Sigma}^{-1}
      \widetilde{\Sigma}^*_{ou} \end{bmatrix}_{st_1,st_2} \right)
    \right]
    \end{split}
    \label{eqn:MSPE_LTA}
    \\
    \begin{split}
      \MSPE\left( \zhathatLTA{ub} \right) \approx& \frac{1}{T^2} \sum_{t_1=1}^T \sum_{t_2=1}^T
      \zhathat{ub}{}\!\!\!\!(s,t_1) \zhathat{ub}{}\!\!\!\!(s,t_2)
      \exp\left( \begin{bmatrix} \widetilde{\Sigma}^*_{uu} 
      \end{bmatrix}_{st_1,st_2} \right)
      \\ &
      \biggl[ 1 - \exp\Bigl( 
        -\COV\left( y^*(s,t_1), y^*(s,t_2) \middle\vert Y, \Psi\right)  
        \Bigr)
        \\ & \phantom{\biggl[}
          \Bigl( 
          e^{ \begin{bmatrix} \Lambda_z \end{bmatrix}_{st_1,st_2}}
          + e^{ \begin{bmatrix} \Lambda_z^\trsp \end{bmatrix}_{st_1,st_2}}
          - e^{ \begin{bmatrix} \Lambda_z \end{bmatrix}_{st_1,st_2} +
            \begin{bmatrix} \Lambda_z^\trsp \end{bmatrix}_{st_1,st_2} }
          \Bigr) \biggr]
    \end{split}
    \label{eqn:MSPE_LTA_ub}
    \\
    \begin{split}
      \MSPE\left( \zhathatLTA{me} \right) \approx& \frac{1}{T^2} \sum_{t_1=1}^T \sum_{t_2=1}^T
      \zhathat{ub}{}\!\!\!\!(s,t_1) \zhathat{ub}{}\!\!\!\!(s,t_2)
      \exp\left( \begin{bmatrix} \widetilde{\Sigma}^*_{uu} 
      \end{bmatrix}_{st_1,st_2} \right)
      \\ &
      \biggl[ 1 - \exp\Bigl( 
        -\COV\left( y^*(s,t_1), y^*(s,t_2) \middle\vert Y, \Psi\right)  
        \Bigr) \biggr]
    \end{split}
    \label{eqn:MSPE_LTA_ms}
  \end{align}
\end{subequations}
Here $\begin{bmatrix} \bullet \end{bmatrix}_{st_1,st_2}$ denotes elements in the
$\bullet$-matrix; to make $\Lambda_z$ a $T \times T$-matrix $\widetilde{X}_u$
and $\widetilde{\Sigma}^*_{uo}$ in \eqref{eqn:Lambda} should now contain
covariates for all times over which we are averaging.

\subsection{Cross-Validation} \label{sec:CV}
The model's predictive accuracy can be assessed through cross-validation.
In \citet{Lindstrom13} a cross-validation setup is presented; the setup,
implemented in this package, handles the highly unbalanced sampling design
and the MESA Air study's interest in predicting long term average exposures.

Dividing the observed locations into groups, $n$-fold cross-validation, consisting
of parameter estimation and predictions, is performed as usual
\citep[Ch.~7.10]{HastieTibshiraniFriedman01}. Given the predictions and
prediction variances, coverage of 95\% prediction intervals, root mean squared
error ($\text{RMSE}$) and cross-validated $R^2$'s,
\begin{equation}
R^2 = \max\biggl(0, 1 - \frac{\text{RMSE}^2}{\VAR(y(s,t))}\biggr),
\label{eqn:R2}
\end{equation}
can be computed.

When assessing predictions of temporal averages at each location, the possibility of
missing observations and the resulting missmatch between averages over available
observations and averages over all predictions must be considered. To account
for this, the averages in \eqref{eqn:LTA} are adjusted to include only observed
time-points
\begin{equation}
\overline{y}(s) = \sum_{t \in \{\tau\,:\,\exists y(s,\tau)\} } \frac{ y(s,t) }
{\norm{ \{\tau\,:\,\exists y(s,\tau)\} }}.
\label{eqn:LTA_obs_only}
\end{equation}

For locations with only a few observations a large part of the $R^2$ may be
attributable to the temporal variability. In \citet{Lindstrom13} the temporal
and spatial effects were separated by  replacing $\VAR(y(s,t))$ in
\eqref{eqn:R2} with the MSE of a reference model. Suggested reference models
were: 
1) the spatial average at each time-point based on observations from all
locations with good temporal coverage; 
2) the observation from the closest available location with good temporal
coverage; 
3) smooth temporal trends fitted to data from the closest location with good
temporal coverage.
The resulting $R^2$'s represent the improvement in predictions
provided by this model, compared to central location or nearest 
neighbour schemes commonly used in epidemiology studies 
\citep{Pope95, Miller07}.


\section{Package Features} \label{sec:features}
The \proglang{R}-package \pkg{SpatioTemporal} includes functions for
estimation (\code{estimate.STmodel} and \code{MCMC.STmodel}),
prediction (\code{predict.STmodel}), 
cross-validation (\code{estimateCV.STmodel} and \code{predictCV.STmodel}),
and simulation (\code{simulate.STmodel}) 
of the model described in Section~\ref{sec:theory}. 
In addition to these functions the package also contains functions for
construction (\code{createSTdata} and \code{createSTmodel}) of objects
encapsulating model definition and data; plotting and evaluation of both data
and results; and functions (see Section~\ref{sec:Ex_trends}) that compute and
evaluate the smooth temporal basis functions described in 
Section~\ref{sec:temporal_basis}.

To reduce computational times matrix identities, such as
\eqref{eqn:sigma_nu_woodbury}, have been used when appropriate and functions
from the \pkg{Matrix}-package \citep{RpkgMatrix} as well as some custom
\proglang{C}-functions have been utilized for sparse and block matrix computations.

\subsection{Key Assumptions}
By construction, the model contains several key assumptions, including:
1) All temporal structure is captured by the smooth temporal basis functions,
2) Spatial dependencies (in the coefficients of the temporal functions) can be
described using stationary universal Kriging
3) The residual $\nu(s,t)$-field is homoscedastic and independent in time; this
will often require roughly equidistant temporal sampling, with occasional
missing time points included for prediction but treated as unobserved.
4) When computing temporal averages the addition of many unobserved times-points
will result in an average that tends to
$$
\overline{y}(s) = \frac{1}{T} \sum_{i=1}^{m} \beta_i(s) \int_0^T f_i(t) \md t,
$$
eliminating any contribution from the $\nu(s,t)$-field; this is an effect of the
assumption of temporal independence in the $\nu(s,t)$-field.


\section[Example: Analysis of Los Angeles NOx Data]{Example: Analysis of Los
  Angeles \no{x} Data} \label{sec:example}
An example analysis of a small \no{x} data set from Los Angeles is used to
illustrate features of the \pkg{SpatioTemporal}-package. The data, which
is included as an example in the pacakge, is a subset of data available to the
MESA Air study; a detailed description of the full dataset can be found in
\citet{Cohen09}.

Following a short description of the data (Section~\ref{sec:data}), we illustrate
how to:
\ref{sec:Ex_initial}) collect data into the \code{S3}-structure used by the package,
\ref{sec:Ex_trends}) construct and evaluate the smooth temporal basis functions,
\ref{sec:Ex_model}) specify the covariates and covariances structures of the
model in \eqref{eqn:beta_fields} and \eqref{eqn:nu_fields},
\ref{sec:Ex_estimation}) estimate parameters and do predictions,
and
\ref{sec:Ex_CV}) evaluate the model using cross-validation.


\subsection{Data} \label{sec:data}

\subsubsection[NOx Observations]{\no{x} Observations}
The data used in this example consists of {\em log-transformed} 2-week average
\no{x} concentrations (ppb) in Los Angelse; observed at 20 locations from the
national AQS (Air Quality System) network of regulatory monitors as well as
at 5 locations from the supplementary MESA Air monitoring.

The national AQS network of regulatory monitors consists of a modest number of
fixed sites that measure ambient concentrations of several different air
pollutants including \no{x}. The MESA Air supplementary monitoring consists of
three sub-campaigns, \citep[see][for details]{Cohen09}: ``fixed sites'', ``home
outdoor'', and ``community snapshot''. Only data from the ``fixed sites'' have
been included in this tutorial; this campaign consisted of five fixed site
monitors that provided 2-week averages during the entire MESA Air monitoring
period. To allow for comparison of the different monitoring protocols, one of
the MESA Air fixed sites in coastal Los Angeles was colocated with an existing
AQS monitor.

\subsubsection{Covariates}
To model the \no{x} data, and to predict at unobserved locations, a number of
geographic and spatio-temporal covariates will be utilized. Geographic
covariates used in this example are: 
1) distance to major roads, i.e.\ census feature class code A1--A3
 (distances truncated to be $\geq\! 10$m and log-transformed);
2) distance to the closest road, i.e.\ the minimum of distances in 1) above;
3) distance to coast (truncated to be $\leq\! 15$km); and
4) average population density in a 2 km buffer.
Here census feature class code A1 roads refer to interstates and other limited
access highways; A2 are primary roads without limited access; and A3 are
secondary roads, e.g.\ state highways \citep[see pp.\ 3--27 in][]{USCensusBureau02}.
For details on the variable selection process that lead to these covariates as
well as a more complete list of the covariates available to MESA Air the reader
is referred to \citet{Mercer11}.

In addition to the geographic covariates a spatio-temporal covariate in the form
of output from a deterministic air pollution model is also available.
The spatio-temporal covariate is the output from a slightly modified version of
Caline3QHC \citep{EPAcaline92,Wilton10,MESAcaline10}. Caline is a line
dispersion model for air pollution. Given locations of major (road) sources and
local meteorology Caline uses a Gaussian model dispersion to predict how
nonreactive pollutants travel with the wind away from sources; providing hourly
estimates of air pollution at distinct points. The hourly contributions from
Caline have been averaged to produce a 2-week average spatio-temporal
covariate. The Caline predictions in this tutorial only
includes air pollution due to traffic on major roads (A1, A2, and large A3).

\subsection[Creating an STdata-Object]{Creating an \code{STdata}-Object} 
\label{sec:Ex_initial}
To get started we load the package, along with a few additional packages and the
data used in this example:
<<>>=
library(SpatioTemporal)
library(Matrix)
library(plotrix) 
library(maps)
data(mesa.data.raw, package="SpatioTemporal")
@ 
Here the raw data contains observations along with geographic and spatio-temporal
covariates. 

The first step is to create an \code{STdata} \code{S3}-object from
the raw data. This object collects observations, covariates and temporal trends
and is used as input to several of the functions in \pkg{SpatioTemporal}.
<<>>=
mesa.data <- createSTdata(obs=mesa.data.raw$obs, covars=mesa.data.raw$X, 
   SpatioTemporal=list(lax.conc.1500=mesa.data.raw$lax.conc.1500))
@ 
Here the observations, \code{mesa.data.raw\$obs}, can be given either as a (number
of time-points) -- by -- (number of locations) matrix, where the location and
time of each observation is given by row- and columnames of the matrix and missing
observations are denote by \code{NA}; or as a \code{data.frame} with fields
\code{date}, \code{ID} and \code{obs}. 

Geographic covariates and locations of the observation are given as a
\code{data.frame}, \code{mesa.data.raw\$X}, and matched to the 
observations through either 1) a field named \code{ID}, or 2) the rownames of
the \code{data.frame}. All observations {\em must be associated} with a row in 
\code{mesa.data.raw\$X}; additional rows giving covariates for
unobserved locations at which we want predictions may be included in
\code{mesa.data.raw\$X}.
If a field \code{type} exists in \code{mesa.data.raw\$X}
it is used to denote which type of monitoring system each location belongs to. In
this example:
<<>>=
table(mesa.data.raw$X$type)
@ 
The spatio-temporal covariates can be given either as a
\code{list} of matrices or as a 3D-\code{array}. The row- and column names
should provide the dates and locations of the spatio-temporal covariates.

The \code{STdata} object has \code{print}, \code{summary}, \code{plot},
\code{qqnorm} and \code{scatterPlot} methods that can be used for exploratory
analysis of the data. For example \code{print(mesa.data)} provides an overview of
observations and covariates while the plot functions can be used to illustrate
which space-time locations that have been observed, investigate the Gaussianity
of our data, or study the dependence between observations and covariates, see
Figure~\ref{fig:plotSTdata}.
<<label=figPlotSTdata, eval=FALSE>>=
layout(matrix(c(1,2,1,3), 2, 2))
par(mar=c(2.3,3.3,2,1), mgp=c(2,1,0))
plot(mesa.data, "loc", main="Occurrence of Observations", xlab="", 
     ylab="Location", col=c("black", "red"), legend.loc=NULL)
par(mar=c(3.3,3.3,2,1))
qqnorm(mesa.data, line=1)
scatterPlot(mesa.data, covar="km.to.coast", xlab="Distance to coast",
            ylab="NOx (log ppb)", pch=19, cex=.25,
            smooth.args=list(span=4/5,degree=2))
@ 

\begin{figure}[!thb]
\centering
<<fig=TRUE, echo=FALSE, height=4>>=
<<figPlotSTdata>>
@
\caption{Counterclockwise from the top: Space-time locations of our
  observations divided into AQS (black) and MESA fixed (red) locations,
  \code{qqnorm}-plot for all observations, and dependence between observations
  and distance to coast.}
\label{fig:plotSTdata}
\end{figure}

\subsection{Temporal Basis Functions} \label{sec:Ex_trends}
Having collected the data, we are now ready to evaluate the
temporal structure and specify temporal basis functions. The algorithms
described in Section~\ref{sec:temporal_basis} are implemented in \code{SVDmiss},
\code{SVDsmooth}, and \code{SVDsmoothCV}; \code{calcSmoothTrends} and
\code{updateTrend} provide tools for altering the smooth temporal basis
functions of \code{STdata}- and \code{STmodel}-objects.

To estimate the smooth temporal functions we first need to construct a
data-matrix \eqref{eqn:data_matrix}
<<>>=
D <- createDataMatrix(mesa.data)
@
For highly unbalanced measurement designs a \code{subset} option exists. This
can be used to restrict the data matrix to only contain observations from
some locations.

\subsubsection{Determining the Number of Basis Functions}
Here the temporal functions will be based on all available data. To determine a
suitable number of basis functions the cross-validation described in
Section~\ref{sec:temporal_basis} is run, evaluting $0$ to $4$ smooth basis
functions (i.e.\ $m=1,\ldots,5$, since $f_1(t)\equiv 1$).
<<echo=FALSE>>=
SVD.cv <- SVDsmoothCV(D, 0:4)
@
<<eval=FALSE>>=
SVD.cv <- SVDsmoothCV(D, 0:4)
@
The output of \code{SVDsmoothCV} consists of the cross-validated MSE, $R^2$,
AIC, and BIC computed for each column using \code{summary.lm} and
\code{extractAIC}. Averaging over all columns/cross-validation groups we have
<<>>=
print(SVD.cv)
@
with a graphical summary obtained from (see Figure~\ref{fig:svdCV})
<<label=figSVDcv, eval=FALSE>>=
plot(SVD.cv)
@
As would be expected in any regression scenario, increasing the number of basis
functions increases $R^2$ and decreases the MSE. All four statistics flatten
out noticable after 2 basis functions, indicating that 2 basis functions
is likely to provide the most efficient description of the temporal
variability. The lack of auto-correlation when fitting the temporal basis to
data at each location (Figure~\ref{fig:smooth_trends}) shows that 2 basis
functions are sufficient to capture the temporal structure.

\begin{figure}[!thb]
\centering
<<fig=TRUE, echo=FALSE, height=4>>=
par(mgp=c(2,1,0))
<<figSVDcv>>
@
\caption{Cross-validation results for different numbers of smooth temporal trends}
\label{fig:svdCV}
\end{figure}

We now use the \code{updateTrend} function to add the smooth temporal basis functions
to the \code{STdata}-object
<<>>=
mesa.data <- updateTrend(mesa.data, n.basis=2)
@
Alternatively \code{calcSmoothTrends} can be used to compute both the basis
functions based on all data and those obtained when excluding each column in the
data-matrix
<<echo=FALSE>>=
smooth.trend <- calcSmoothTrends(mesa.data, n.basis=2, cv=TRUE)
@
<<eval=FALSE>>=
smooth.trend <- calcSmoothTrends(mesa.data, n.basis=2, cv=TRUE)
@
This allows for a sensitivity analysis of the temporal basis functions. Here we
illustrate this by the fit at one location
(Figure~\ref{fig:smooth_trends_CV}), but the different temporal basis functions
could be carried through the entire analysis.
<<label=figsmooth_trends_CV, eval=FALSE>>=
mesa.data.cv <- vector("list",  length(smooth.trend$trend.fnc.cv))
for(i in 1:length(mesa.data.cv)){
  suppressMessages(mesa.data.cv[[i]] <- updateTrend(mesa.data, 
                   fnc=smooth.trend$trend.fnc.cv[[i]]))
}
plot(mesa.data, main="Possible temporal trends",
     xlab="", ylab="NOx (log ppb)", pch=c(19,NA), cex=.25)
for(i in 1:length(mesa.data.cv)){
  plot(mesa.data.cv[[i]], add=TRUE, col=i, pch=NA, lty=c(NA,2))
}
@

\begin{figure}[!thb]
\centering
<<fig=TRUE, echo=FALSE, height=2.5>>=
par(mar=c(2.1,3.3,2,1), mgp=c(2,1,0))
<<figsmooth_trends_CV>>
@
\caption{Cross-validated temporal basis functions. The solid line is the
  regression fit of 2 basis functions, based on all data, to observations at
  AQS site \Sexpr{mesa.data$covars$ID[1]}, the dashed lines are the
  corresponding fits of basis functions obtained when excluding one column at a
  time from the data-matrix.}
\label{fig:smooth_trends_CV}
\end{figure}

\subsubsection{Evaluating the Basis Functions}
The \code{plot.STdata} function can now be used to evaluate how well the
temporal basis functions capture the temporal structure, see
Figure~\ref{fig:smooth_trends}. \code{plot.STdata} fits a linear regression of
observations for a particular location of the smooth basis functions, and plots
fitted values, residuals, auto-correlation, or partial auto-correlation functions.
Here the two temporal basis functions capture the temporal variability in the data,
as illustrated by residuals and correlation functions.
<<label=figsmooth_trends, eval=FALSE>>=
par(mar=c(3.3,3.3,1.5,1), mgp=c(2,1,0))
layout(matrix(c(1,1,2,2,3,4), 3, 2, byrow=TRUE))
plot(mesa.data, "obs", ID="60370113", 
     xlab="", ylab="NOx (log ppb)",
     main="Temporal trend 60370113")
plot(mesa.data, "res", ID="60370113", 
     xlab="", ylab="NOx (log ppb)")
plot(mesa.data, "acf", ID="60370113")
plot(mesa.data, "pacf", ID="60370113")
@

\begin{figure}[!thb]
\centering
<<fig=TRUE, echo=FALSE, height=5>>=
<<figsmooth_trends>>
@
\caption{The smooth temporal trends fitted to data at site
  \Sexpr{mesa.data$covars$ID[5]}. Fitted trends, residuals, auto-correlation, and
  partial auto-correlation functions are shown.}
\label{fig:smooth_trends}
\end{figure}

\subsubsection{Deterministic Basis Functions}
In lieu of the SVD based basis functions we could use a set of deterministic
temporal functions, e.g.\ $f_1(t)=1$, $f_2(t)=2\pi t/365$,
$f_3(t)=\sin(2\pi t/365)$, and $f_4(t)=\cos(2\pi t/365)$. Specifying
these and comparing to the data driven smooths extrated above (see
Figure~\ref{fig:deterministic_trends}) we note that, for this data, three
deterministic basis functions achieve a slightly worse fit than the two
functions based on smoothed SVDs.
<<label=figDeterministicTrends, eval=FALSE>>=
mesa.data.fnc <- updateTrend(mesa.data, fnc=function(x){
  x = 2*pi*as.numeric(x)/365; 
  return( cbind(x, sin(x), cos(x)) )})
par(mfrow=c(2,1), mar=c(2.3,3.3,1.5,1), mgp=c(2,1,0))
for(i in c("60370016","60371103")){
  plot(mesa.data, ID=i, pch=c(19,NA), cex=.25, xlab="",
       ylab="NOx (log ppb)", main=paste("AQS site",i))
  plot(mesa.data.fnc, ID=i, add=TRUE, col=2, pch=NA)
}
@ 

\begin{figure}[!thb]
\centering
<<fig=TRUE, echo=FALSE, height=3>>=
<<figDeterministicTrends>>
@
\caption{Comparing the fit of 2 smooth (black) and 3 deterministic (red) basis
  functions at two locations.}
\label{fig:deterministic_trends}
\end{figure}


\subsection{Specifying the Model} \label{sec:Ex_model}
Having constructed a \code{STdata}-object with suitable temporal basis functions
we are now ready to create a \code{STmodel}-object that can be used by the
estimation and prediction functions of \pkg{SpatioTemporal}. 
A \code{STmodel}-object is created through \code{createSTmodel}, by adding
covariance and covariate specifications for the $\beta$- and $\nu$-fields to a
\code{STdata}-object.


Suitable covariates and covariance structures for the $\beta$-fields in
\eqref{eqn:beta_fields} can be determined by considering the empirical estimates
of these fields obtained by regressing the observations at each location
on the temporal basis functions. The resulting regression coefficients can be
analysed using standard geo-statistical software, e.g.\ provided by the
\proglang{R}-packages \pkg{geoR} \citep{RpkgGeoR} or \pkg{fields}
\citep{RpkgFields}, to determine suitable mean and covariance models \citep[see
also][]{Mercer11}.

Here we briefly illustrate the point by computing the regression coefficents and
comparing them to a few possible covariates (Figure~\ref{fig:naive_beta}). 
<<label=figBetaLm, eval=FALSE>>=
beta.lm <- estimateBetaFields(mesa.data)
par(mfrow=c(1,2), mar=c(3.3,2.3,1.5,1), mgp=c(2,1,0))
plotCI(mesa.data$covars$log10.m.to.a1, beta.lm$beta[,1], 
       uiw=1.96*beta.lm$beta.sd[,1], ylab="", xlab="Distance to A1-road",
       main="Beta-field for f1(t)")
plotCI(mesa.data$covars$km.to.coast, beta.lm$beta[,2], 
       uiw=1.96*beta.lm$beta.sd[,2], ylab="", xlab="Distance to coast",
       main="Beta-field for f2(t)")
@ 
To simplify the analysis of unbalanced measurement designs \code{estimateBetaFields}
provides a \code{subset} option that restricts the evaluated locations.
\begin{figure}[!thb]
\centering
<<fig=TRUE, echo=FALSE, height=3>>=
<<figBetaLm>>
@
\caption{Regression estimates of $\beta_1(s)$ and $\beta_2(s)$ at each location,
  with $95\%$ confidence intervals, as a function of the distance to A1 roads or
  the distance to coast.} 
\label{fig:naive_beta}
\end{figure}

Partially based on Figure~\ref{fig:naive_beta} we specify different mean models
for the three $\beta$-fields, but use an exponential covariance function without
nugget for all fields; it is possible to specify different covariance functions for
each $\beta$-field. Possible covariance functions are describe by the functions
\code{namesCovFuns} and \code{parsCovFuns}, while \code{updateCovf} can be used
to alter the covariance structure of an existing \code{STmodel}.
<<>>=
LUR <- list(~log10.m.to.a1+s2000.pop.div.10000+km.to.coast,
            ~km.to.coast, ~km.to.coast)
cov.beta <- list(covf="exp", nugget=FALSE)
@ 
For the $\nu$-field we use an exponential covariance with a nugget that
can differ between AQS and MESA fixed sites.
<<>>=
cov.nu <- list(covf="exp", nugget=~type, random.effect=FALSE)
@ 
In this specification the $\log$-nugget will be given by a linear model
\begin{equation}
\log \sigma^2_\text{nugget}(s) = \theta_{\nu,\text{const}} +
  \theta_{\nu,\text{type}} \mathbf{1}(s \in \text{MESA fixed sites}).
  \label{eqn:nu_nugget}
\end{equation}
The \code{random.effect=FALSE} option specifies that
we do not want a random mean for each $\nu(\cdot,t)$ field. A random effect
in time provides a way of capturing temporally uncorrelated large scale
deviations from the smooth temporal basis functions. Using both a regression
model for $\log$-nugget \eqref{eqn:nu_nugget} and a temporal random effect the
full spatio-temporal covariance for the $\nu$-field would be
\begin{equation}
  r_\nu(s_1,t_1; s_2,t_2) = 
  \begin{cases}
    \sigma^2_\text{nugget}(s_1) + g(0) + \tau^2, & \text{if } s_1=s_2,\ t_1=t_2, \\
    g(s_1-s_2) + \tau^2, & \text{if } s_1 \neq s_2,\ t_1=t_2, \\
    0, & \text{if } t_1 \neq t_2,
  \end{cases}
  \label{eqn:nu_covf}
\end{equation}
where $\sigma^2_\text{nugget}(s)$ is a (spatially varying) nugget, $g(s)$ is a
stationary covariance function, and $\tau^2$ is the variance of a temporal
random effect.

The final step is to specify coordinates for the observations and create
the \code{STmodel}-object:
<<>>=
locations <- list(coords=c("x","y"), long.lat=c("long","lat"), 
                  others="type")
mesa.model <- createSTmodel(mesa.data, LUR=LUR, ST="lax.conc.1500",
                            cov.beta=cov.beta, cov.nu=cov.nu,
                            locations=locations)
@ 
In \code{locations} the element \code{coords} specifies which components of
\code{mesa.data\$covars} to use when computing distances between observation
locations; the elements \code{long.lat} and \code{others} give additional fields
that are carried over from \code{mesa.data\$covars} to
\code{mesa.model\$locations}, e.g.\ data that could be useful for plotting.
All variables referenced in the \code{LUR} and \code{cov.nu\$nugget}
formulas, and in the \code{locations}-list must be found in
\code{mesa.data\$covars}.

Most of the \code{S3}-methods available for \code{STdata}-objects also exists
for \code{STmodel}-objects.

\subsection{Parameter Estimation and Prediciton} \label{sec:Ex_estimation}
Parameters of the spatio-temporal model \eqref{eqn:distr_Y} can by obtained by
maximising \eqref{eqn:profile_likelihood} through \code{estimate.STmodel}. Given
(estimated) parameters predictions are obtained from \code{predict.STmodel}.

\subsubsection{Estimation}
To avoid potential numerical optimisation issues, the estimation function allows
for multiple starting points, returning all optima found. The functions
\code{loglikeSTdim} and \code{loglikeSTnames} gives the number of parameters
(and other model dimension) and the names, i.e.\ expected order, of the
parameters. Using this information a two column matrix, where each column
represents a different optimisation starting point, is constructed
<<>>=
dim <- loglikeSTdim(mesa.model)
x.init <- cbind(c( rep(2, dim$nparam.cov-1), 0),
                c( rep(c(1,-3), dim$m+1), -3, 0))
rownames(x.init) <- loglikeSTnames(mesa.model, all=FALSE)
@ 
Model parameters are then estimated through
<<eval=FALSE>>=
est.mesa.model <- estimate(mesa.model, x.init, type="p", hessian.all=TRUE)
@ 
where \code{type} allows us to use either the f(ull), p(rofile)
\eqref{eqn:profile_likelihood}, or r(eml) \eqref{eqn:REML_likelihood}
log-likelihoods. Since the estimation takes time ($10$--$15$ minutes, depending
on the computer) the package contains precomputed results which we load and
examine.
<<>>=
data(est.mesa.model, package="SpatioTemporal")
print(est.mesa.model)
@ 
The estimation results indicate that the optimisation for both starting points
have converged, starting point 1 being marginally better, with very similar
parameter estimates and function values.

Plots of the estimated parameters together with approximate $95\%$-confidence
intervals computed using the Hessian, i.e.\ the observed information matrix
\citep[Ch.~10]{CasellaBerger02}, can be seen in Figure~\ref{fig:CVparest} below;
this Figure also includes parameter estimates from the cross-validation.
Confidence intervals for covariance parameters of the residual $\nu$-field are
much smaller than the corresponding intervals for the covariance parameters
characterising the $\beta$-fields. This is due to us having {\em only one
  replicate} of each $\beta$-field --- in principal given by the regression of
observations on the smooth temporal basis functions --- but $T=\Sexpr{dim$T}$
replicates of the residual $\nu$-field, {\em one for each timepoint} --- in
principal given by the residuals from the regression.
%$

\subsubsection{Predictions}
Given estimated parameters, predictions can be computed for the Gaussian model using
\eqref{eqn:EX}; for log-Gaussian processes using \eqref{eqn:EZ} or
\eqref{eqn:EZ_REML}; and for the temporal averages using \eqref{eqn:EX_LTA} or
\eqref{eqn:log_LTA}. 
<<echo=FALSE>>=
pred <- predict(mesa.model, est.mesa.model, LTA=TRUE, type="p")
pred.log <- predict(mesa.model, est.mesa.model, LTA=TRUE,
                    transform="unbiased", type="p")
@ 
<<eval=FALSE>>=
pred <- predict(mesa.model, est.mesa.model, LTA=TRUE, type="p")
pred.log <- predict(mesa.model, est.mesa.model, LTA=TRUE,
                    transform="unbiased", type="p")
@ 
Here the \code{type} option controls the computation of variances, with 
\code{type="p"} (or \code{type="f"}) the regression parameters are assumed known
resulting in variances/MSPEs according to \eqref{eqn:VX_Y} or \eqref{eqn:MSPE};
for \code{type="r"} regression parameters are assumed unknown and variances/MSPEs
are given by \eqref{eqn:VX_REML_Y} or \eqref{eqn:MSPE_REML}.
Items computed include predictions of $y(s,t)$, contributions to the predictions
from different parts of the model (see Figure~\ref{fig:predict_example} above),
prediction variances (or MSPE), and predictions of the $\beta$-fields;
\code{print.predictSTmodel} can be used to display all computed components.

The latent $\beta$-fields \eqref{eqn:EX_beta} can be compared to the empirical,
regression based, estimates presented in Figure~\ref{fig:naive_beta}. Comparing
these two estimates (see Figure~\ref{fig:beta_est}) we see that they are close,
with the largest discrepancies occuring for $\beta$'s associated with the second
temporal trend, $f_3(t)$. However, the uncertainty for these $\beta$'s are
substantial.
<<label=figBetaEX, eval=FALSE>>=
par(mfrow=c(2,2), mar=c(3.3,3.3,1.5,1), mgp=c(2,1,0), pty="s")
for(i in 1:3){
  plotCI(x=beta.lm$beta[,i], y=pred$beta$EX[,i], 
         uiw=1.96*beta.lm$beta.sd[,i], err="x", 
         pch=NA, sfrac=0.005,
         main=paste("Beta-field for f", i, "(t)", sep=""),
         xlab="Empirical estimate",
         ylab="Spatio-Temporal Model")
  plotCI(x=beta.lm$beta[,i], y=pred$beta$EX[,i], 
         uiw=1.96*sqrt(pred$beta$VX[,i]),
         add=TRUE, pch=NA, sfrac=0.005)
  abline(0, 1, col="grey")
}
@ 

\begin{figure}[!thb]
\centering
<<fig=TRUE, echo=FALSE, height=6>>=
<<figBetaEX>>
@
\caption{Comparison of the regression based and full \protect{\eqref{eqn:EX_beta}}
  estimates of the $\beta$--field, including the uncertainties in both estimates.}
\label{fig:beta_est}
\end{figure}

The predictions also include the temporal averages at each location. In 
Figure~\ref{fig:LTA} these predictions are compared to the temporal averages of
observations at each site. A minor issue (see also Section~\ref{sec:CV}) with this
comparison is the temporal miss-match between predictions (averaged over
10-years) and the observations (averaged over 1 to 10 years depending on
available data).
<<label=figLTA, eval=FALSE>>=
par(mfrow=c(1,2), mar=c(3.3,3.3,1.5,1), mgp=c(2,1,0))
with(pred$LTA, plotCI(colMeans(D, na.rm=TRUE), EX, uiw=1.96*sqrt(VX.pred),
                      xlim=c(2.9,4.4), ylim=c(2.9,4.4),
                      ylab="Predictions", xlab="Observations",
                      main="Average NOx (log ppb)"))
abline(0, 1, col="grey")
with(pred.log$LTA, plotCI(colMeans(exp(D), na.rm=TRUE), 
                          EX, uiw=1.96*sqrt(VX.pred),
                          xlim=c(25,95), ylim=c(25,95),
                          ylab="Predictions", xlab="Observations",
                          main="Average NOx (ppb)"))
abline(0, 1, col="grey")
@ 

\begin{figure}[!thb]
\centering
<<fig=TRUE, echo=FALSE, height=3>>=
<<figLTA>>
@
\caption{Observed and predicted temporal averages at each location, both for the
  Gaussian (left) and log-Gaussian (right) processes. The difference between
  observed and predicted values is (partially) due to temporally incomplete
  observations, i.e.\ the predicted average is over the \emph{entire} $10$-year
  period, regardless of when we have observations.}
\label{fig:LTA}
\end{figure}


\subsection{Cross-validation and Model Evaluation} \label{sec:Ex_CV}
The model's predictive ability can be evaluated through cross-validataion
\citep{Lindstrom13}. The first step is to define a vector that splits the
observations into CV-groups
<<>>=
Ind.cv <- createCV(mesa.model, groups=10, min.dist=.1)
@ 
For the $i^\text{th}$ CV-group observations for which $\text{Ind.cv} = i$ are
predicted using parameter estimates and conditional expectations based on
the observations where $\text{Ind.cv} \neq i$. Since the primary focus when
constructing the package was spatial prediction the number of locations in each
group will be roughly even
<<>>=
ID.cv <- sapply(split(mesa.model$obs$ID, Ind.cv), unique)
print( sapply(ID.cv, length) )
@ 
although the number of observations in each grup may differ substantially
<<>>=
table(Ind.cv)
@ 
Having four locations in the $10^\text{th}$ group is due to the fact that
AQS site \code{60371103} is colocated with MESA Air site \code{L001}.
<<>>=
print(mesa.model$D.beta[ID.cv[[10]],ID.cv[[10]]])
@
As seen from the distance matrix above, the two stations are only
$\Sexpr{round(mesa.model$D.beta["60371103","L001"],3)}$ km apart (less than
\code{min.dist=.1}) causing \code{createCV} to treat them as ``one'' location.

For cases where the primary interest is in temporal predictions suitable
\code{Ind.cv} vectors could be construted with the help of elements in
\code{mesa.model$obs}. 
%$

\subsubsection{Estimation and Prediciton}
For the estimation part of the cross-validation a set of starting point(s) is
needed; here we use the optimum found in the optimisation and the starting
point, of the two possible, which lead to the optimum.
<<>>=
x.init <- coef(est.mesa.model, pars="cov")[,c("par","init")]
@ 
As before the estimation is computationally expensive
<<eval=FALSE>>=
est.cv.mesa <- estimateCV(mesa.model, x.init, Ind.cv)
@ 
and we load precomputed results.
<<>>=
data(est.cv.mesa, package="SpatioTemporal")
@ 
Studying results from \code{print(est.cv.mesa)} (not shown) it can be seen that
all 10 of the estimations have converged, and comparing these estimates
(available through \code{coef(est.cv.mesa)}) to those obtained for the entire data
set shows reasonable agrement, both with regards to values and uncertainties
(Figure~\ref{fig:CVparest}). 
<<label=figParEstCV, eval=FALSE>>=
par(mfrow=c(1,1), mar=c(13.5,2.5,.5,.5), las=2)
with(coef(est.mesa.model, pars="all"), 
     plotCI((1:length(par))+.3, par, uiw=1.96*sd, 
            col=2, xlab="", xaxt="n", ylab=""))
boxplot(est.cv.mesa, "all", boxwex=.4, col="grey", add=TRUE)
@
\begin{figure}[!thb]
<<fig=TRUE, echo=FALSE, height=6>>=
<<figParEstCV>>
@ 
\caption{Estimated parameters and approximate 95\% confidence intervals (red)
  compared to parameter estimates from 10-fold cross-validation (box-plots).}
\label{fig:CVparest}
\end{figure}

Given parameter estimates the prediction part of the cross-validation can be
carried out for either Gaussian or log-Gaussian models. In both
cases temporal averages are computed based on only the observed time-points, as
in \eqref{eqn:LTA_obs_only}.
<<eval=TRUE>>=
##pred.cv.mesa <- predictCV(mesa.model, est.cv.mesa, LTA=TRUE)
data(pred.cv.mesa, package="SpatioTemporal")
pred.cv.mesa.log <- predictCV(mesa.model, est.cv.mesa, 
                         LTA=TRUE, transform="unbiased")
@ 
As for \code{estimateCV} the computations take time and precomputed results are
included in \code{data(CV.mesa.model)} which we loaded previously.

Alternatively the function \code{computeLTA} can be applied to the result from
\code{predictCV}, however this will only compute the temporal averages
in \eqref{eqn:LTA_obs_only} and will \emph{not} include the variances from
\eqref{eqn:VX_LTA} or \eqref{eqn:MSPE_LTA}-\eqref{eqn:MSPE_LTA_ms}.

\subsubsection{Model Evaluation}
The \code{summary.predCVSTmodel} function computes RMSE, $R^2$, and coverage of
prediction intervals for the cross-validation. For the log-Gaussian data we have:
<<>>=
summary(pred.cv.mesa.log)
@ 
The results show reasonable models fits with $R^2$ values of 
$\Sexpr{round(summary(pred.cv.mesa.log)$R2[1,4],3)}$ and
$\Sexpr{round(summary(pred.cv.mesa.log)$R2[2,4],3)}$ 
for observations and temporal averages respectively. It should also be noted
that although the mean component,
\begin{equation*}
\exp\left( \M\widehat{\mv{\gamma}} + FX\widehat{\mv{\alpha}} \right),
\end{equation*}
accounts for a substantial part of the $R^2$ 
($\Sexpr{round(summary(pred.cv.mesa.log)$R2[1,1],2)}$)
for the observations, it captures very little of the variability in the temporal
averages
($R^2 \approx \Sexpr{round(summary(pred.cv.mesa.log)$R2[2,1],2)}$).
The $\Sexpr{round(summary(pred.cv.mesa.log)$coverage["average",1]*100)}\%$ coverage
of a $95\%$ confidence interval for the temporal averages might seem a tad low, but
one should remember that it translates to
$\Sexpr{summary(pred.cv.mesa.log)$coverage["average",1]*dim$n}$ of $\Sexpr{dim$n}$
locations. 

To seperate temporal and spatial predictive ability for locations with few
observations (not an issue here), the reference models described in
\citet{Lindstrom13} can be computed by \code{predictNaive}, and including these
in the \code{summary}-call above gives adjusted $R^2$-values. Further
\code{summary(pred.cv.mesa.log, by.date=TRUE)} provides individual cross-validation
statistics for each time-point. The result from \code{predCV} contains, in
\code{pred.cv.mesa.log\$pred.obs}, a \code{data.frame} with observations,
predictions, variances, etc.\ that can be used to compute additional
cross-validation statistics. 

To visualise the cross-validation results, several different plots are available
(Figure~\ref{fig:CVpred}); both for the original (ppb, log-Gaussian) and
the transformed (log ppb, Gaussian) data. 
Studying time-series at single locations, both the predictions and $95\%$
prediction intervals agree well with left-out observations. 
Plotting all predictions against observations also shows reasonable agreement,
although some locations exhibit small, but consistent, biases.
The predicted temporal averages \eqref{eqn:LTA_obs_only} match the
observations, but with rather large prediction intervals. The width of the
intervals is caused by several locations only having a few years of
data to average over. The normalised residuals $(y-\E(y))/\sqrt{\VAR(y)}$ are
approximately $\Normal{0}{1}$, with no strong seasonal differences, indicating
that our distributional assumptions are reasonable. Finally plots of residuals
agains covariates show little unexplained, residual structure.

\clearpage

<<label=figPredCV, eval=FALSE>>=
par(mar=c(3.3,3.3,1.5,1), mgp=c(2,1,0))
layout(matrix(c(1,1,2,2,3,4,5,6), 4, 2, byrow=TRUE))
plot(pred.cv.mesa, ID="60371601", xlab="", ylab="NOx (log ppb)", 
     main="Predictions for 60371601", lty=c(1,NA), lwd=2, 
     pch=c(NA,19), cex=.75)
plot(pred.cv.mesa, ID="60371601", pred.type="EX.mu", 
     lty=4, lwd=2, col="blue", add=TRUE)
plot(pred.cv.mesa, ID="60371601", pred.type="EX.mu.beta", 
     lty=2, lwd=2, col="green", add=TRUE)

plot(pred.cv.mesa.log, ID="60371601", xlab="", ylab="NOx (ppb)", 
     main="Predictions for 60371601", pred.type="EX.pred", 
     lty=c(1,NA), lwd=2, pch=c(NA,19), cex=.75)
plot(pred.cv.mesa.log, ID="60371601", pred.type="EX.mu", 
     lty=4, lwd=2, col="blue", add=TRUE)
plot(pred.cv.mesa.log, ID="60371601", pred.type="EX.mu.beta", 
     lty=2, lwd=2, col="green", add=TRUE)
legend("topright", c("Observations", "Predictions",
                     "Contribution from beta",
                     "Contribution from mean",
                     "95% CI"), bty="n",
       lty=c(NA,1,2,4,NA), lwd=c(NA,2,2,2,NA),
       pch=c(19,NA,NA,NA,15), pt.cex=c(.75,NA,NA,NA,2.5),
       col=c("red", "black", "green", "blue", "grey"))

plot(pred.cv.mesa, "obs", ID="all", pch=c(19,NA), cex=.25, lty=c(NA,2), 
     col=c("ID", "black", "grey"), xlab="Observations", 
     ylab="Predictions", main="Cross-validation NOx (log ppb)")

with(pred.cv.mesa.log$pred.LTA, plotCI(obs, EX.pred, uiw=1.96*sqrt(VX.pred),
                                  xlab="Observations", ylab="Predictions",
                                  main="Temporal average NOx (ppb)"))
abline(0, 1, col="grey")

I.season <- as.factor(as.POSIXlt(pred.cv.mesa$pred.obs$date)$mon+1)
levels(I.season) <- c(rep("Winter",2), rep("Spring",3), 
                      rep("Summer",3), rep("Fall",3), "Winter") 
qqnorm(pred.cv.mesa, norm=TRUE, main="Normalised residuals",
       col=I.season)
legend("bottomright", legend=as.character(levels(I.season)),
       pch=1, col=1:nlevels(I.season), bty="n")

scatterPlot(pred.cv.mesa, STdata=mesa.model, covar="log10.m.to.a1", 
            group=I.season, col=c(2:5,1), type="res",
            xlab="Distance to A1-Road", ylab="Residuals", 
            main="Residuals (log ppb)")
@

\begin{figure}[!thb]
<<fig=TRUE, echo=FALSE, height=8>>=
<<figPredCV>>
@ 
\caption{The two top panes shows predictions for a left-out location, in
  $\log$ and original scale (cf.\ Figure~\protect{\ref{fig:predict_example}}).
  In the left pane of the third row predictions have been plotted against
  observations; the points are coloured by location and grouping of data from
  single locations can be seen. 
  On the right predictions and observations of temporal
  averages \protect{\eqref{eqn:LTA_obs_only}} are compared.
  In the last row, the left pane shows a QQ-plot for normalised
  residuals for the Gaussian model, coloured by season; the solid line gives
  the theoretical behaviour of $\Normal{0}{1}$ residuals.
  On the right, residuals (coloured by season) are plotted as a function of
  distance to A1-roads; smooths, for each season and all data, have been
  added to help identify any remaining structure.}
\label{fig:CVpred}
\end{figure}

\clearpage

\section{Outlook} \label{sec:Outlook}
Having introduced the \proglang{R}-package \pkg{SpatioTemporal} and illustrated
the main features of the package, we now discuss one possible future
extension of the model.

The computational feasibility of the model implemented here relies on
the simplification of \eqref{eqn:full_likelihood} using, mainly, the matrix
identity in \eqref{eqn:sigma_nu_woodbury}. This simplification
will only lead to reduced computational cost if $\Sigma_\nu$ has a structure,
e.g.\ block-diagonal, that allows for fast computations of $\Sigma_\nu^{-1}$.
To obtain this block-diagonal structure an assumption of temporal independence
in the residual $\nu(s,t)$-field is required. In the future we hope to relax
this assumption, allowing for a temporally correlated
$\nu(s,t)$-field. Computational feasibility can hopefully be retained by
obtaining a sparse $\Sigma_\nu$ matrix through tapering \citep{Furrer06}.

\section*{Acknowledgements}
Data used in the example has been provided by  {\bf the Multi-Ethnic Study 
of Atherosclerosis and Air Pollution (MESA Air)}. Details regarding the data
can be found in \citet{Cohen09,Wilton10}.

Although this tutorial and development of the package described there in
has been funded wholly or in part by the United States Environmental 
Protection Agency through {\bf assistance agreement CR-834077101-0} 
and {\bf grant RD831697} to the University of Washington, it has not 
been subjected to the Agency's required peer and policy review and 
therefore does not necessarily reflect the views of the Agency 
and no official endorsement should be inferred.

Travel for Johan Lindstr\"om has been paid by 
{\bf STINT Grant IG2005-2047}.

Additional funding was provided by grants to the University of 
Washington from the {\bf National Institute of Environmental Health 
Sciences (P50 ES015915)} and the {\bf Health Effects Institute 
(4749-RFA05-1A/06-10)}.

%%%%%%BIBLIOGRAPHY%%%%%%%%%
\bibliography{tutorial}

\end{document}
