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\title{Moran Eigenvectors\footnote{This vignette formed pp. 302--305 of the first edition of Bivand, R. S.,
Pebesma, E. and G\'{o}mez-Rubio V. (2008) Applied Spatial Data Analysis with R,
Springer-Verlag, New York. It was retired from the second edition (2013) to
accommodate material on other topics, and is made available in this form
with the understanding of the publishers.}} 
\author{Roger Bivand} 

\begin{document} 

\maketitle 

<<echo=FALSE>>= 
owidth <- getOption("width")
options("width"=90)
ow <- getOption("warn")
options("warn"=-1)
.PngNo <- 0
@

<<label=afig,echo=FALSE,eval=FALSE>>= 
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pdf(file=file, width = 6.5, height = 3.5, pointsize = 12, bg = "white")
opar <- par(mar=c(3,3,1,1)+0.1)
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pdf(file=file, width = 6.5, height = 5, pointsize = 12, bg = "white")
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.PngNo <- .PngNo + 1; file <- paste("Fig-bitmap-", .PngNo, ".pdf", sep="")
pdf(file=file, width = 6.5, height = 5, pointsize = 12, bg = "white")
opar <- par(mar=c(3,3,1,1)+0.1)
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par(opar)
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<<label=zfigl,echo=FALSE,eval=FALSE>>=
dev.null <- dev.off()
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@

\section{Introduction}

The
Moran eigenvector approach \citep{dray+legendre+peres-neto:06,
griffith+peres-neto:06} involved the spatial patterns represented by maps
of eigenvectors; by choosing suitable orthogonal patterns and adding
them to a linear or generalised linear model, the spatial dependence
present in the residuals can be moved into the model.

It uses brute force to search the set of eigenvectors of the
matrix $\mathbf{M W M}$, where

\[
\mathbf{M} = \mathbf{I} - \mathbf{X}(\mathbf{X}^{\rm T}
\mathbf{X})^{-1}\mathbf{X}^{\rm T}
\]
\noindent is a symmetric and idempotent projection matrix and
$\mathbf{W}$ are the spatial weights. In the spatial lag form of
\code{SpatialFiltering} and in the GLM \code{ME} form below,
$\mathbf{X}$ is an $n$-vector of ones, that is the intercept
only.

In its general form, \code{SpatialFiltering} chooses the subset of the
$n$ eigenvectors that reduce the residual spatial autocorrelation in
the error of the model with covariates. The lag form adds the covariates
in assessment of which eigenvectors to choose, but does not use them in
constructing the eigenvectors. \code{SpatialFiltering} was implemented
and contributed by Yongwan Chun and Michael Tiefelsdorf, and is presented
in \citet{tiefelsdorf+griffith:07}; \code{ME} is based on Matlab code by
Pedro Peres-Neto and is discussed in \citet{dray+legendre+peres-neto:06}
and \citet{griffith+peres-neto:06}.

\begin{footnotesize}
<<echo=TRUE>>= 
library(maptools)
library(spdep)
owd <- getwd()
setwd(system.file("etc/shapes", package="spdep"))
NY8 <- readShapeSpatial("NY8_utm18")
setwd(system.file("etc/weights", package="spdep"))
NY_nb <- read.gal("NY_nb.gal", region.id=row.names(NY8))
setwd(owd)
@
\end{footnotesize}


\begin{footnotesize}
<<echo=TRUE,eval=TRUE>>= 
nySFE <- SpatialFiltering(Z~PEXPOSURE+PCTAGE65P+PCTOWNHOME, data=NY8, nb=NY_nb, style="W", verbose=FALSE)
nylmSFE <- lm(Z~PEXPOSURE+PCTAGE65P+PCTOWNHOME+fitted(nySFE), data=NY8)
summary(nylmSFE)
nylm <- lm(Z~PEXPOSURE+PCTAGE65P+PCTOWNHOME, data=NY8)
anova(nylm, nylmSFE)
@
\end{footnotesize}


Since the \code{SpatialFiltering} approach does not allow weights
to be used, we see that the residual autocorrelation of the
original linear model is absorbed, or `whitened' by the inclusion
of selected eigenvectors in the model, but that the covariate
coefficients change little. The addition of these eigenvectors --
each representing an independent spatial pattern -- relieves the
residual autocorrelation, but otherwise makes few changes in the
substantive coefficient values.

The \code{ME} function also searches for eigenvectors from the
spatial lag variant of the underlying model, but in a GLM
framework. The criterion is a permutation bootstrap test on
Moran's $I$ for regression residuals, and in this case, because
of the very limited remaining spatial autocorrelation, is set at
$\alpha = 0.5$. Even with this very generous stopping rule, only
few eigenvectors are chosen; their combined contribution only just
improves the fit of the GLM model.

\begin{footnotesize}
<<echo=TRUE>>= 
NYlistwW <- nb2listw(NY_nb, style = "W")
set.seed(111)
<<echo=TRUE,eval=FALSE>>= 
nyME <- ME(Cases~PEXPOSURE+PCTAGE65P+PCTOWNHOME, data=NY8, offset=log(POP8), family="poisson", listw=NYlistwW, alpha=0.5)
<<echo=FALSE,eval=TRUE>>=
bsfn <- system.file("doc/backstore/nyME_res.RData", package="spdep")
load(bsfn)
@
<<echo=TRUE>>=
nyME
NY8$eigen_24 <- fitted(nyME)[,1]
NY8$eigen_223 <- fitted(nyME)[,2]
@
\end{footnotesize}


\begin{figure}[!t]
\centering
<<results=tex,echo=FALSE>>= 
.iwidth <- 6
.iheight <- 4
.ipointsize <- 10
<<afig>>
library(RColorBrewer)
#gry <- brewer.pal(9, "Greys")[-1]
spplot(NY8, c("eigen_24", "eigen_223"), col.regions=grey.colors(6, 0.95, 0.55, 2.2), cuts=5)
<<zfig>>
@ 
\caption{Maps
of the two eigenvalues selected for inclusion in the Poisson
regression model} \label{fig:geigen2}
\end{figure}

\begin{footnotesize}
<<echo=TRUE,eval=TRUE>>= 
nyglmME <- glm(Cases~PEXPOSURE+PCTAGE65P+PCTOWNHOME+offset(log(POP8))+fitted(nyME), data=NY8, family="poisson")
summary(nyglmME)
nyGLMp <- glm(Cases~PEXPOSURE+PCTAGE65P+PCTOWNHOME+offset(log(POP8)), data=NY8,family="poisson")
anova(nyGLMp, nyglmME, test="Chisq")
@
\end{footnotesize}

Figure~\ref{fig:geigen2} shows the spatial patterns chosen to
match the very small amount of spatial autocorrelation remaining
in the model. As with the other Poisson regressions, the
closeness to TCE sites is highly significant. Since, however,
many TCE sites are also in or close to more densely populated
urban areas with the possible presence of both point-source and
non-point-source pollution, it would be premature to take such
results simply at their face value. There is, however, a
potentially useful contrast between the cities of Binghampton in
the south of the study area with several sites in its vicinity,
and Syracuse in the north without TCE sites in this data set.

\begin{thebibliography}{}

\bibitem[Dray et~al., 2006]{dray+legendre+peres-neto:06}
Dray, S., Legendre, P., and Peres-Neto, P.~R. (2006).
 Spatial modeling: A comprehensive framework for principle coordinate
  analysis of neighbor matrices ({PCNM}).
 {\em Ecological Modelling}, 196:483--493.

\bibitem[Griffith and Peres-Neto, 2006]{griffith+peres-neto:06}
Griffith, D.~A. and Peres-Neto, P.~R. (2006).
 Spatial modeling in ecology: The flexibility of eigenfunction spatial
  analyses.
 {\em Ecology}, 87:2603--2613.

\bibitem[Tiefelsdorf and Griffith, 2007]{tiefelsdorf+griffith:07}
Tiefelsdorf, M. and Griffith, D.~A. (2007).
 Semiparametric filtering of spatial autocorrelation: The eigenvector
  approach.
 {\em Environment and Planning A}, 39:1193--1221.

\end{thebibliography}

\end{document}


