---
title: "“The Problem of Spatial Autocorrelation:” forty years on"
author: "Roger Bivand"
output:
  html_document:
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
    toc_depth: 2
bibliography: refs.bib
vignette: >
  %\VignetteIndexEntry{The Problem of Spatial Autocorrelation:” forty years on}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
--- 


```{r setup, include=FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
```

## Introduction

@cliff+ord:69, published forty years ago, marked a turning point in the
treatment of spatial autocorrelation in quantitative geography. It
provided the framework needed by any applied researcher to attempt an
implementation for a different system, possibly using a different
programming language. In this spirit, here we examine how spatial
weights have been represented in implementations and may be reproduced,
how the tabulated results in the paper may be reproduced, and how they
may be extended to cover simulation.

One of the major assertions of @cliff+ord:69 is that their statistic
advances the measurement of spatial autocorrelation with respect to
@moran:50 and @geary:54 because a more general specification of spatial
weights could be used. This more general form has implications both for
the preparation of the weights themselves, and for the calculation of
the measures. We will look at spatial weights first, before moving on to
consider the measures presented in the paper and some of their
subsequent developments. Before doing this, we will put together a data
set matching that used in @cliff+ord:69. They provide tabulated data for
the counties of the Irish Republic, but omit Dublin from analyses. A
shapefile included in this package, kindly made available by Michael
Tiefelsdorf, is used as a starting point:

```{r echo=TRUE,eval=TRUE,results='hide'}
library(spdep)
if (require(rgdal, quietly=TRUE)) {
  eire <- readOGR(system.file("shapes/eire.shp", package="spData")[1])
} else {
  require(maptools, quietly=TRUE)
  eire <- readShapeSpatial(system.file("shapes/eire.shp", package="spData")[1])
}
row.names(eire) <- as.character(eire$names)
proj4string(eire) <- CRS("+proj=utm +zone=30 +ellps=airy +units=km")
```
```{r echo=TRUE,eval=TRUE}
class(eire)
names(eire)
```

and read into a SpatialPolygonsDataFrame — classes used for handling
spatial data in are fully described in @bivandetal:08. To this we need
to add the data tabulated in the paper in Table 2,[^1] p. 40, here in
the form of a text file with added rainfall values from Table 9, p. 49:

```{r echo=TRUE,eval=TRUE}
fn <- system.file("etc/misc/geary_eire.txt", package="spdep")[1]
ge <- read.table(fn, header=TRUE)
names(ge)
```

Since we assigned the county names as feature identifiers when reading
the shapefiles, we do the same with the extra data, and combine the
objects:

```{r echo=TRUE,eval=TRUE}
row.names(ge) <- as.character(ge$county)
all.equal(row.names(ge), row.names(eire))
library(maptools)
eire_ge <- spCbind(eire, ge)
```

Finally, we need to drop the Dublin county omitted in the analyses
conducted in @cliff+ord:69:

```{r echo=TRUE,eval=TRUE}
eire_ge1 <- eire_ge[!(row.names(eire_ge) %in% "Dublin"),]
length(row.names(eire_ge1))
```

To double-check our data, let us calculate the sample Beta coefficients,
using the formulae given in the paper for sample moments:

```{r echo=TRUE,eval=TRUE}
skewness <- function(z) {z <- scale(z, scale=FALSE); ((sum(z^3)/length(z))^2)/((sum(z^2)/length(z))^3)}
kurtosis <- function(z) {z <- scale(z, scale=FALSE); (sum(z^4)/length(z))/((sum(z^2)/length(z))^2)}
```

These differ somewhat from the ways in which skewness and kurtosis are
computed in modern statistical software, see for example
@joanes+gill:98. However, for our purposes, they let us reproduce Table
3, p. 42:

```{r echo=TRUE,eval=TRUE}
print(sapply(as(eire_ge1, "data.frame")[13:24], skewness), digits=3)
print(sapply(as(eire_ge1, "data.frame")[13:24], kurtosis), digits=4)
print(sapply(as(eire_ge1, "data.frame")[c(13,16,18,19)], function(x) skewness(log(x))), digits=3)
print(sapply(as(eire_ge1, "data.frame")[c(13,16,18,19)], function(x) kurtosis(log(x))), digits=4)
```

Using the tabulated value of $23.6$ for the percentage of agricultural
holdings above 50 in 1950 in Waterford, the skewness and kurtosis cannot
be reproduced, but by comparison with the [`irishdata`]{} dataset in ,
it turns out that the value should rather be $26.6$, which yields the
tabulated skewness and kurtosis values.

Before going on, the variables considered are presented in Table
\[vars\].

  variable         description
  ---------------- -----------------------------------------------------------------------------
  pagval2\_10      Percentage number agricultural holdings in valuation group £2–£10 (1950)
  pagval10\_50     Percentage number agricultural holdings in valuation group £10–£50 (1950)
  pagval50p        Percentage number agricultural holdings in valuation group above £50 (1950)
  cowspacre        Milch cows per 1000 acres crops and pasture (1952)
  ocattlepacre     Other cattle per 1000 acres crops and pasture (1952)
  pigspacre        Pigs per 1000 acres crops and pasture (1952)
  sheeppacre       Sheep per 1000 acres crops and pasture (1952)
  townvillp        Town and village population as percentage of total (1951)
  carspcap         Private cars registered per 1000 population (1952)
  radiopcap        Radio licences per 1000 population (1952)
  retailpcap       Retail sales £ per person (1951)
  psinglem30\_34   Single males as percentage of all males aged 30–34 (1951)
  rainfall         Average of rainfall for stations in Ireland, 1916–1950, mm

  : Description of variables in the Geary data set.[]{data-label="vars"}

## Spatial weights

As a basis for comparison, we will first read the unstandardised
weighting matrix given in Table A1, p. 54, of the paper, reading a file
corrected for the misprint giving O rather than D as a neighbour of V:

```{r echo=TRUE,eval=TRUE}
fn <- system.file("etc/misc/unstand_sn.txt", package="spdep")[1]
unstand <- read.table(fn, header=TRUE)
summary(unstand)
```

In the file, the counties are represented by their serial letters, so
ordering and conversion to integer index representation is required to
reach a representation similar to that of the SpatialStats module for
spatial neighbours:

```{r echo=TRUE,eval=TRUE}
class(unstand) <- c("spatial.neighbour", class(unstand))
of <- ordered(unstand$from)
attr(unstand, "region.id") <- levels(of)
unstand$from <- as.integer(of)
unstand$to <- as.integer(ordered(unstand$to))
attr(unstand, "n") <- length(unique(unstand$from))
```

Having done this, we can change its representation to a [`listw`]{}
object, assigning an appropriate style (generalised binary) for
unstandardised values:

```{r echo=TRUE,eval=TRUE}
lw_unstand <- sn2listw(unstand)
lw_unstand$style <- "B"
lw_unstand
```

Note that the values of S0, S1, and S2 correspond closely with those
given on page 42 of the paper, $0.84688672$, $0.01869986$ and
$0.12267319$. The discrepancies appear to be due to rounding in the
printed table of weights.

The contiguous neighbours represented in this object ought to match
those found using [`poly2nb`]{}. However, we see that the reproduced
contiguities have a smaller link count:

```{r echo=TRUE,eval=TRUE}
nb <- poly2nb(eire_ge1)
nb
```

The missing link is between Clare and Kerry, perhaps by the
Tarbert–Killimer ferry, but the counties are not contiguous, as Figure
\[plot\_nb\] shows:

```{r echo=TRUE,eval=TRUE}
xx <- diffnb(nb, lw_unstand$neighbours, verbose=TRUE)
```
```{r echo=TRUE,eval=FALSE,results='hide'}
plot(eire_ge1, border="grey60")
plot(nb, coordinates(eire_ge1), add=TRUE, pch=".", lwd=2)
plot(xx, coordinates(eire_ge1), add=TRUE, pch=".", lwd=2, col=3)
```

```{r eval=TRUE,echo=FALSE, fig.cap="County boundaries and contiguities"}
par(mfrow=c(1,2))
plot(eire_ge1, border="grey40")
title(xlab="25 Irish counties")
text(coordinates(eire_ge1), labels=as.character(eire_ge1$serlet), cex=0.8)
plot(eire_ge1, border="grey60")
title(xlab="Contiguities")
plot(nb, coordinates(eire_ge1), add=TRUE, pch=".", lwd=2)
plot(xx, coordinates(eire_ge1), add=TRUE, pch=".", lwd=2, col=3)
legend("topleft", legend=c("Contiguous", "Ferry"), lwd=2, lty=1, col=c(1,3), bty="n", cex=0.7)
par(mfrow=c(1,1))
``` 

An attempt has also been made to reproduce the generalised weights for
25 Irish counties reported by @cliff+ord:69, after Dublin is omitted.
Reproducing the inverse distance component $d_{ij}^{-1}$ of the
generalised weights $d_{ij}^{-1} \beta_{i(j)}$ is eased by the statement
in @cliff+ord:73 [p. 55] that the points chosen to represent the
counties were their “geographic centres,” so not very different from the
centroids yielded by applying a chosen computational geometry function.
The distance metric is not given, and may have been in kilometers or
miles — both were tried, but the results were not sensitive to the
difference as it applies equally across the weights; miles are used
here. Computing the proportion of shared distance measure $\beta_{i(j)}$
is harder, because it requires the availability of the full topology of
the input polygons. @bivandetal:08 [p. 244] show how to employ the
[`vect2neigh`]{} function (written by Markus Neteler) in the package
when using GRASS GIS vector handling to create a full topology from
spaghetti vector data and to extract border segment lengths; a similar
approach also is mentioned there using ArcGIS coverages for the same
purpose. GRASS was used to create the topology, and next the proportion
of shared distance measure was calculated.

```{r echo=FALSE,eval=TRUE}
load(system.file("etc/misc/raw_grass_borders.RData", package="spdep")[1])
```
```{r echo=TRUE,eval=FALSE,results='hide'}
library(maptools)
SG <- Sobj_SpatialGrid(eire_ge1)$SG
library(spgrass6)
grass_home <- "/home/rsb/topics/grass/g64/grass-6.4.0svn"
initGRASS(grass_home, home=tempdir(), SG=SG, override=TRUE)
writeVECT6(eire_ge1, "eire", v.in.ogr_flags=c("o", "overwrite"))
res <- vect2neigh("eire", ID="serlet")
```
```{r echo=TRUE,eval=TRUE}
grass_borders <- sn2listw(res)
raw_borders <- grass_borders$weights
int_tot <- attr(res, "total") - attr(res, "external")
prop_borders <- lapply(1:length(int_tot), function(i) raw_borders[[i]]/int_tot[i])
dlist <- nbdists(grass_borders$neighbours, coordinates(eire_ge1))
inv_dlist <- lapply(dlist, function(x) 1/(x/1.609344))
combo_km <- lapply(1:length(inv_dlist), function(i) inv_dlist[[i]]*prop_borders[[i]])
combo_km_lw <- nb2listw(grass_borders$neighbours, glist=combo_km, style="B")
summary(combo_km_lw)
```

To compare, we need to remove the Tarbert–Killimer ferry link manually,
and view the summary of the original weights, as well as a correlation
coefficient between these and the reconstructed weights. Naturally,
unless the boundary coordinates used here are identical with those in
the original analysis, presumably measured by hand, small differences
will occur.

```{r echo=TRUE,eval=TRUE}
red_lw_unstand <- lw_unstand
Clare <- which(attr(lw_unstand, "region.id") == "C")
Kerry <- which(attr(lw_unstand, "region.id") == "H")
Kerry_in_Clare <- which(lw_unstand$neighbours[[Clare]] == Kerry)
Clare_in_Kerry <- which(lw_unstand$neighbours[[Kerry]] == Clare)
red_lw_unstand$neighbours[[Clare]] <- red_lw_unstand$neighbours[[Clare]][-Kerry_in_Clare]
red_lw_unstand$neighbours[[Kerry]] <- red_lw_unstand$neighbours[[Kerry]][-Clare_in_Kerry]
red_lw_unstand$weights[[Clare]] <- red_lw_unstand$weights[[Clare]][-Kerry_in_Clare]
red_lw_unstand$weights[[Kerry]] <- red_lw_unstand$weights[[Kerry]][-Clare_in_Kerry]
summary(red_lw_unstand)
cor(unlist(red_lw_unstand$weights), unlist(combo_km_lw$weights))
```

Even though the differences in the general weights, for identical
contiguities, are so small, the consequences for the measure of spatial
autocorrelation are substantial, Here we use the fifth variable, other
cattle per 1000 acres crops and pasture (1952), and see that the
reconstructed weights seem to “reveal” more autocorrelation than the
original weights.

```{r echo=TRUE,eval=TRUE}
flatten <- function(x, digits=3, statistic="I") {
  res <- c(format(x$estimate, digits=digits),
    format(x$statistic, digits=digits),
    format.pval(x$p.value, digits=digits))
  res <- matrix(res, ncol=length(res))
  colnames(res) <- paste(c("", "E", "V", "SD_", "P_"), "I", sep="")
  rownames(res) <- deparse(substitute(x))
  res
}
`reconstructed weights` <- moran.test(eire_ge1$ocattlepacre, combo_km_lw)
`original weights` <- moran.test(eire_ge1$ocattlepacre, red_lw_unstand)
print(rbind(flatten(`reconstructed weights`), flatten(`original weights`)), quote=FALSE)
```

## Measures of spatial autocorrelation

Our targets for reproduction are Tables 4 and 5 in @cliff+ord:69 [pp.
43–44], the first containing standard deviates under normality and
randomisation for the original Moran measure with binary weights, the
original Geary measure with binary weights, the proposed measure with
unstandardised general weights, and the proposed measure with
row-standardised general weights. In addition, four variables were
log-transformed on the basis of the skewness and kurtosis results
presented above. We carry out the transformation of these variables, and
generate additional binary and row-standardised general spatial weights
objects — note that the weights constants for the row-standardised
general weights agree with those given on p. 42 in the paper, after
allowing for small differences due to rounding in the weights values
displayed in the paper (p. 54):

```{r echo=TRUE,eval=TRUE}
eire_ge1$ln_pagval2_10 <- log(eire_ge1$pagval2_10)
eire_ge1$ln_cowspacre <- log(eire_ge1$cowspacre)
eire_ge1$ln_pigspacre <- log(eire_ge1$pigspacre)
eire_ge1$ln_sheeppacre <- log(eire_ge1$sheeppacre)
vars <- c("pagval2_10", "ln_pagval2_10", "pagval10_50", "pagval50p",
 "cowspacre", "ln_cowspacre", "ocattlepacre", "pigspacre",
 "ln_pigspacre", "sheeppacre", "ln_sheeppacre", "townvillp",
 "carspcap", "radiopcap", "retailpcap", "psinglem30_34")
nb_B <- nb2listw(lw_unstand$neighbours, style="B")
nb_B
lw_std <- nb2listw(lw_unstand$neighbours, glist=lw_unstand$weights, style="W")
lw_std
```

The standard representation of the measures is:

$$I = \frac{n}{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}}
\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}(x_i-\bar{x})(x_j-\bar{x})}{\sum_{i=1}^{n}(x_i - \bar{x})^2}$$

for Moran’s $I$ — in the paper termed the proposed statistic, and for
Geary’s $C$:

$$C = \frac{(n-1)}{2\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}}
\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}(x_i-x_j)^2}{\sum_{i=1}^{n}(x_i - \bar{x})^2}$$

where $x_i, i=1, \ldots, n$ are $n$ observations on the numeric variable
of interest, and $w_{ij}$ are the spatial weights. In order to reproduce
the standard deviates given in the paper, it is sufficient to apply
[`moran.test`]{} to the variables with three different spatial weights
objects, and two different values of the [`randomisation=`]{} argument.
In addition, [`geary.test`]{} is applied to a single spatial weights
objects, and two different values of the [`randomisation=`]{} argument.

```{r echo=TRUE,eval=TRUE}
system.time({
MoranN <- lapply(vars, function(x) moran.test(eire_ge1[[x]], listw=nb_B, randomisation=FALSE))
MoranR <- lapply(vars, function(x) moran.test(eire_ge1[[x]], listw=nb_B, randomisation=TRUE))
GearyN <- lapply(vars, function(x) geary.test(eire_ge1[[x]], listw=nb_B, randomisation=FALSE))
GearyR <- lapply(vars, function(x) geary.test(eire_ge1[[x]], listw=nb_B, randomisation=TRUE))
Prop_unstdN  <- lapply(vars, function(x) moran.test(eire_ge1[[x]], listw=lw_unstand, randomisation=FALSE))
Prop_unstdR  <- lapply(vars, function(x) moran.test(eire_ge1[[x]], listw=lw_unstand, randomisation=TRUE))
Prop_stdN  <- lapply(vars, function(x) moran.test(eire_ge1[[x]], listw=lw_std, randomisation=FALSE))
Prop_stdR  <- lapply(vars, function(x) moran.test(eire_ge1[[x]], listw=lw_std, randomisation=TRUE))
})
res <- sapply(c("MoranN", "MoranR", "GearyN", "GearyR", "Prop_unstdN", "Prop_unstdR", "Prop_stdN", "Prop_stdR"), function(x) sapply(get(x), "[[", "statistic"))
rownames(res) <- vars
ores <- res[,c(1,2,5:8)]
```

In order to conduct 8 different tests on 16 variables, we use
[`lapply`]{} on the list of variables in the specified order, then
[`sapply`]{} on a list of output objects by name to generate a table in
the same row and column order as the original (we save a copy of six
columns for comparison with bootstrap results below):

```{r echo=FALSE,eval=TRUE}
options("width"=100)
```
```{r echo=TRUE,eval=TRUE}
print(formatC(res, format="f", digits=4), quote=FALSE)
```
```{r echo=FALSE,eval=TRUE}
options("width"=90)
```

The values of the standard deviates agree with those in Table 4 in the
original paper, with the exception of those for the proposed statistic
with standardised weights under normality for all untransformed
variables. We can see what has happened by substituting the weights
constants for the standardised weights with those for unstandardised
weights:

```{r echo=TRUE,eval=TRUE}
wc_unstd <- spweights.constants(lw_unstand)
wrong_N_sqVI <- sqrt((wc_unstd$nn*wc_unstd$S1 - wc_unstd$n*wc_unstd$S2 + 3*wc_unstd$S0*wc_unstd$S0)/((wc_unstd$nn-1)*wc_unstd$S0*wc_unstd$S0)-((-1/(wc_unstd$n-1))^2))
raw_data <- grep("^ln_", vars, invert=TRUE)
I <- sapply(Prop_stdN, function(x) x$estimate[1])[raw_data]
EI <- sapply(Prop_stdN, function(x) x$estimate[2])[raw_data]
res <- (I - EI)/wrong_N_sqVI
names(res) <- vars[raw_data]
print(formatC(res, format="f", digits=4), quote=FALSE)
```

Next, let us look at Table 5 in the original paper. Here we only
tabulate the values of the measures themselves, and, since the
expectation is constant for each measure, the square root of the
variance of the measure under randomisation — extracting values
calculated above:

```{r echo=TRUE,eval=TRUE}
res <- lapply(c("MoranR", "GearyR", "Prop_unstdR", "Prop_stdR"), function(x) sapply(get(x), function(y) c(y$estimate[1], sqrt(y$estimate[3]))))
res <- t(do.call("rbind", res))
colnames(res) <- c("I", "sigma_I", "C", "sigma_C", "unstd_r", "sigma_r", "std_r", "sigma_r")
rownames(res) <- vars
print(formatC(res, format="f", digits=4), quote=FALSE)
```

The values are as follows, and match the original with the exception of
those for the initial version of Moran’s $I$ in the first two columns.
If we write a function implementing equations 3 and 4:

$$I = \frac{\sum_{i=1}^{n}\sum_{j=i+1}^{n}w_{ij}(x_i-\bar{x})(x_j-\bar{x})}{\sum_{i=1}^{n}(x_i - \bar{x})^2}$$

where crucially the inner summation is over $i+1 \ldots n$, not
$1 \ldots
n$, we can reproduce the values of the measure shown in the original
Table 5:

```{r echo=TRUE,eval=TRUE}
oMoranf <- function(x, nb) {
  z <- scale(x, scale=FALSE)
  n <- length(z)
  glist <- lapply(1:n, function(i) {ii <- nb[[i]]; ifelse(ii > i, 1, 0)})
  lw <- nb2listw(nb, glist=glist, style="B")
  wz <- lag(lw, z)
  I <- (sum(z*wz)/sum(z*z))
  I
}
res <- sapply(vars, function(x) oMoranf(eire_ge1[[x]], nb=lw_unstand$neighbours))
print(formatC(res, format="f", digits=4), quote=FALSE)
```

The variance term given in equation 7 in the original paper is for the
case of normality, not randomisation; the reference on p. 28 to equation
38 on p. 26 does not permit the reproduction of the values in the second
column of Table 5. The variance equation given as equation 1.35 by
@cliff+ord:73 [p. 9] does not do so either, so for the time being it is
not possible to say how the tabulated values were computed. Note that
since the standard deviances are reproduced correctly, and can be
reproduced from the second column values using the measure and its
expectance, it is just a matter of establishing which formula was used,
but this has so far not proved possible.

## Simulating measures of spatial autocorrelation

@cliff+ord:69 do not conduct simulation experiments, although their
sequels do, notably @cliff+ord:73, among many others. Simulation studies
are necessarily more demanding computationally, especially if spatially
autocorrelated variables are to be created, as in @cliff+ord:73 [pp.
146–153]. In the same book, they also report the use of permutation
tests, also known as Monte Carlo or Hope hypothesis testing procedures
[@cliff+ord:73 pp. 50–52]. These procedures provided a way to examine
the distribution of the statistic of interest by exchanging at random
the observed values between observations, and then comparing the
simulated distribution under the null hypothesis of no spatial
patterning with the observed value of the statistic in question.

```{r echo=TRUE,eval=TRUE}
MoranI.boot <- function(var, i, ...) {
  var <- var[i]
  return(moran(x=var, ...)$I)
}
Nsim <- function(d, mle) {
  n <- length(d)
  rnorm(n, mle$mean, mle$sd)
}
f_bperm <- function(x, nsim, listw) {
  boot(x, statistic=MoranI.boot, R=nsim, sim="permutation", listw=listw,
    n=length(x), S0=Szero(listw))
}
f_bpara <- function(x, nsim, listw) {
  boot(x, statistic=MoranI.boot, R=nsim, sim="parametric", ran.gen=Nsim,
    mle=list(mean=mean(x), sd=sd(x)), listw=listw, n=length(x),
    S0=Szero(listw))
}
nsim <- 4999
set.seed(1234)
```

First let us define a function [`MoranI.boot`]{} just to return the
value of Moran’s $I$ for variable [`var`]{} and permutation index
[`i`]{}, and a function [`Nsim`]{} to generate random samples from the
variable of interest assuming Normality. To make it easier to process
the variables in turn, we encapsulate calls to [`boot`]{} in wrapper
functions [`f_bperm`]{} and [`f_bpara`]{}. Running 4999 simulations for
each of 16 for three different weights specifications and both
parametric and permutation bootstrap takes quite a lot of time.

```{r echo=TRUE,eval=FALSE}
system.time({
MoranNb <- lapply(vars, function(x) f_bpara(x=eire_ge1[[x]], nsim=nsim, listw=nb_B))
MoranRb <- lapply(vars, function(x) f_bperm(x=eire_ge1[[x]], nsim=nsim, listw=nb_B))
Prop_unstdNb  <- lapply(vars, function(x) f_bpara(x=eire_ge1[[x]], nsim=nsim, listw=lw_unstand))
Prop_unstdRb  <- lapply(vars, function(x) f_bperm(x=eire_ge1[[x]], nsim=nsim, listw=lw_unstand))
Prop_stdNb  <- lapply(vars, function(x) f_bpara(x=eire_ge1[[x]], nsim=nsim, listw=lw_std))
Prop_stdRb  <- lapply(vars, function(x) f_bperm(x=eire_ge1[[x]], nsim=nsim, listw=lw_std))
})
```
```{r echo=FALSE,eval=FALSE}
zzz <- system.time({
MoranNb <- lapply(vars, function(x) f_bpara(x=eire_ge1[[x]], nsim=nsim, listw=nb_B))
MoranRb <- lapply(vars, function(x) f_bperm(x=eire_ge1[[x]], nsim=nsim, listw=nb_B))
Prop_unstdNb  <- lapply(vars, function(x) f_bpara(x=eire_ge1[[x]], nsim=nsim, listw=lw_unstand))
Prop_unstdRb  <- lapply(vars, function(x) f_bperm(x=eire_ge1[[x]], nsim=nsim, listw=lw_unstand))
Prop_stdNb  <- lapply(vars, function(x) f_bpara(x=eire_ge1[[x]], nsim=nsim, listw=lw_std))
Prop_stdRb  <- lapply(vars, function(x) f_bperm(x=eire_ge1[[x]], nsim=nsim, listw=lw_std))
})
res <- lapply(c("MoranNb", "MoranRb", "Prop_unstdNb", "Prop_unstdRb", "Prop_stdNb", "Prop_stdRb"), function(x) sapply(get(x), function(y) (y$t0 - mean(y$t))/sd(y$t)))
res <- t(do.call("rbind", res))
colnames(res) <- c("MoranNb", "MoranRb", "Prop_unstdNb", "Prop_unstdRb", "Prop_stdNb", "Prop_stdRb")
rownames(res) <- vars
save(zzz, res, file="backstore/boot_res.RData")
```
```{r echo=FALSE,eval=FALSE}
bsfn <- system.file("etc/backstore/boot_res.RData", package="spdep")
load(bsfn)
zzz
```
```{r echo=TRUE,eval=FALSE}
res <- lapply(c("MoranNb", "MoranRb", "Prop_unstdNb", "Prop_unstdRb", "Prop_stdNb", "Prop_stdRb"), function(x) sapply(get(x), function(y) (y$t0 - mean(y$t))/sd(y$t)))
res <- t(do.call("rbind", res))
colnames(res) <- c("MoranNb", "MoranRb", "Prop_unstdNb", "Prop_unstdRb", "Prop_stdNb", "Prop_stdRb")
rownames(res) <- vars
```

We collate the results to compare with the analytical standard deviates
under Normality and randomisation, and see that in fact the differences
are not at all large, as expressed by the median absolute difference
between the tables. We can also see that inferences based on a one-sided
$\alpha=0.05$ cut-off are the same for the analytical and bootstrap
approaches. This indicates that we can, in general, rely on the
analytical standard deviates, and that bootstrap methods will not help
if assumptions underlying the measures are not met.

```{r echo=TRUE,eval=TRUE}
print(formatC(res, format="f", digits=4), quote=FALSE)
oores <- ores - res
apply(oores, 2, mad)
alpha_0.05 <- qnorm(0.05, lower.tail=FALSE)
all((res >= alpha_0.05) == (ores >= alpha_0.05))
```

These assumptions affect the shape of the distribution of the measure in
its tails; one possibility is to use a Saddlepoint approximation to find
an equivalent to the analytical or bootstrap-based standard deviate for
inference [@tiefelsdorf:02]. The Saddlepoint approximation requires the
eigenvalues of the weights matrix and iterative root-finding for global
Moran’s $I$, while for local Moran’s $I_i$, analytical forms are known.
Even with this computational burden, the Saddlepoint approximation for
global Moran’s $I$ runs quite quickly. First we need to fit null linear
models (only including an intercept) to the variables, then apply
[`lm.morantest.sad`]{} to the fitted model objects:

```{r echo=TRUE,eval=TRUE}
lm_objs <- lapply(vars, function(x) lm(formula(paste(x, "~1")), data=eire_ge1))
system.time({
MoranSad <- lapply(lm_objs, function(x) lm.morantest.sad(x, listw=nb_B))
Prop_unstdSad  <- lapply(lm_objs, function(x) lm.morantest.sad(x, listw=lw_unstand))
Prop_stdSad  <- lapply(lm_objs, function(x) lm.morantest.sad(x, listw=lw_std))
})
res <- sapply(c("MoranSad", "Prop_unstdSad", "Prop_stdSad"), function(x) sapply(get(x), "[[", "statistic"))
rownames(res) <- vars
```

Although the analytical standard deviates (under Normality) are larger
than those reached using the Saddlepoint approximation when measured by
median absolute deviation, the differences do not lead to different
inferences at this chosen cut-off. This reflects the fact that the shape
of the distribution is very sensitive to small $n$, but for moderate $n$
and global Moran’s $I$, the effects are seen only further out in the
tails. The consequences for local Moran’s $I_i$ are much stronger,
because the clique of neighbours of each observation is typically very
small. It is perhaps of interest that the differences are much smaller
for the case of general weights than for unstandardised binary weights.

```{r echo=TRUE,eval=TRUE}
print(formatC(res, format="f", digits=4), quote=FALSE)
oores <- res - ores[,c(1,3,5)]
apply(oores, 2, mad)
all((res >= alpha_0.05) == (ores[,c(1,3,5)] >= alpha_0.05))
```

In addition we could choose to use the exact distribution of Moran’s
$I$, as described by @tiefelsdorf:00; its implementation is covered in
@bivandetal:09. The global case also needs the eigenvalues of the
weights matrix, and the solution of a numerical integration function,
but for these cases, the timings are quite acceptable.

```{r echo=TRUE,eval=TRUE}
system.time({ 
MoranEx <- lapply(lm_objs, function(x) lm.morantest.exact(x, listw=nb_B))
Prop_unstdEx  <- lapply(lm_objs, function(x) lm.morantest.exact(x, listw=lw_unstand))
Prop_stdEx  <- lapply(lm_objs, function(x) lm.morantest.exact(x, listw=lw_std))
})
res <- sapply(c("MoranEx", "Prop_unstdEx", "Prop_stdEx"), function(x) sapply(get(x), "[[", "statistic"))
rownames(res) <- vars
```

The output is comparable with that of the Saddlepoint approximation, and
the inferences drawn here are the same for the chosen cut-off as for the
analytical standard deviates calculated under Normality.

```{r echo=TRUE,eval=TRUE}
print(formatC(res, format="f", digits=4), quote=FALSE)
oores <- res - ores[,c(1,3,5)]
apply(oores, 2, mad)
all((res >= alpha_0.05) == (ores[,c(1,3,5)] >= alpha_0.05))
```

@lietal:07 take up the challenge in @cliff+ord:69 [p. 31], to try to
give the statistic a bounded fixed range. Their APLE measure is intended
to approximate the spatial dependence parameter of a simultaneous
autoregressive model better than Moran’s $I$, and re-scales the measure
by a function of the eigenvalues of the spatial weights matrix. APLE
requires the use of row standardised weights.

```{r echo=TRUE,eval=TRUE}
vars_scaled <- lapply(vars, function(x) scale(eire_ge1[[x]], scale=FALSE))
nb_W <- nb2listw(lw_unstand$neighbours, style="W")
pre <- spdep:::preAple(0, listw=nb_W)
MoranAPLE <- sapply(vars_scaled, function(x) spdep:::inAple(x, pre))
pre <- spdep:::preAple(0, listw=lw_std, override_similarity_check=TRUE)
Prop_stdAPLE <- sapply(vars_scaled, function(x) spdep:::inAple(x, pre))
res <- cbind(MoranAPLE, Prop_stdAPLE)
colnames(res) <- c("APLE W", "APLE Gstd")
rownames(res) <- vars
```

In order to save time, we use the two internal functions
[`spdep:::preAple`]{} and [`spdep:::inAple`]{}, since for each
definition of spatial weights, the same eigenvalue calculations need to
be made. The notation using the [`:::`]{} operator says that the
function with named after the operator is to be found in the namespace
of the package named before the operator. The APLE values repeat the
pattern that we have already seen — for some variables, the measured
autocorrelation is very similar irrespective of spatial weights
definition, while for others, the change in the definition from binary
to general does make a difference.

```{r echo=TRUE,eval=TRUE}
print(formatC(res, format="f", digits=4), quote=FALSE)
```

```{r results='asis',eval=TRUE,echo=FALSE, fig.cap="Three contrasted spatial weights definitions"}
pal <- grey.colors(9, 1, 0.5, 2.2)
oopar <- par(mfrow=c(1,3), mar=c(1,1,3,1)+0.1)
z <- t(listw2mat(nb_B))
brks <- c(0,0.1,1)
image(1:25, 1:25, z[,ncol(z):1], breaks=brks, col=pal[c(1,9)],
 main="Binary", axes=FALSE)
box()
z <- t(listw2mat(lw_unstand))
brks <- c(0,quantile(c(z)[c(z) > 0], seq(0,1,1/8)))
image(1:25, 1:25, z[,ncol(z):1], breaks=brks, col=pal, main="General", axes=FALSE)
box()
z <- t(listw2mat(lw_std))
brks <- c(0,quantile(c(z)[c(z) > 0], seq(0,1,1/8)))
image(1:25, 1:25, z[,ncol(z):1], breaks=brks, col=pal,
 main="Std. general", axes=FALSE)
box()
par(oopar)
``` 
\caption{Three contrasted spatial weights definitions.}
\label{plot_wts}

```{r results='asis',eval=TRUE,echo=FALSE}
eire_ge1$nb_B <- sapply(nb_B$weights, sum)
eire_ge1$lw_unstand <- sapply(lw_unstand$weights, sum)
library(lattice)
trellis.par.set(sp.theme())
p1 <- spplot(eire_ge1, c("nb_B"), main="Binary")
p2 <- spplot(eire_ge1, c("lw_unstand"), main="General")
print(p1, split=c(1,1,2,1), more=TRUE)
print(p2, split=c(2,1,2,1), more=FALSE)
``` 
\caption{Sums of weights by county for two contrasted spatial weights definitions --- for row standardisation, all counties sum to unity.}
\label{plot_map}

## Odds and ends $\ldots$

The differences found in the case of a few variables in inference using
the original binary weights, and the general weights proposed by
@cliff+ord:69 are necessarily related to the the weights thenselves.
Figures \[plot\_wts\] and \[plot\_map\] show the values of the weights
in sparse matrix form, and the sums of weights by county where these
sums are not identical by design. In the case of binary weights, the
matrix entries are equal, but the sums up-weight counties with many
neighbours.

General weights up-weight counties that are close to each other, have
more neighbours, and share larger boundary proportions (an asymmetric
relationship). There is a further impact of using boundary proportions,
in that the boundary between the county and the exterior is subtracted,
thus boosting the weights between edge counties and their neighbours,
even if there are few of them. Standardised general weights up-weight
further up-weight counties with few neighbours, chiefly those on the
edges of the study area.

With a small data set, here with $n=25$, it is very possible that edge
and other configuration effects are relatively strong, and may impact
inference in different ways. The issue of egde effects has not really
been satisfactorily resolved, and should be kept in mind in analyses of
data sets of this size and shape.

## References

[^1]: cropped scans of tables are available from
    <http://spatial.nhh.no/R/etc/CO69-PNGs.zip>.