eire {spdep} | R Documentation

## Eire data sets

### Description

The Eire data set has been converted to shapefile format and placed in the etc/shapes directory. The initial data objects are now stored as a SpatialPolygonsDataFrame object, from which the contiguity neighbour list is recreated. For purposes of record, the original data set is retained.

The `eire.df` data frame has 26 rows and 9 columns. In addition, polygons of the 26 counties are provided as a multipart polylist in eire.polys.utm (coordinates in km, projection UTM zone 30). Their centroids are in eire.coords.utm. The original Cliff and Ord binary contiguities are in eire.nb.

### Usage

    data(eire)

### Format

This data frame contains the following columns:

A
  Percentage of sample with blood group A
towns
  Towns/unit area
pale
  Beyond the Pale 0, within the Pale 1
size
  number of blood type samples
ROADACC
  arterial road network accessibility in 1961
OWNCONS
  percentage in value terms of gross agricultural output of each county consumed by itself
POPCHG
  1961 population as percentage of 1926
RETSALE
  value of retail sales £000
INCOME
  total personal income £000
names
  County names

### Source

Upton and Fingleton 1985, - Bailey and Gatrell 1995, ch. 1 for blood group data, Cliff and Ord (1973), p. 107 for remaining variables (also after O'Sullivan, 1968). Polygon borders and Irish data sourced from Michael Tiefelsdorf's SPSS Saddlepoint bundle, originally hosted at: http://geog-www.sbs.ohio-state.edu/faculty/tiefelsdorf/GeoStat.htm.

### Examples

    eire <- readShapePoly(system.file("etc/shapes/eire.shp", package="spdep")[1],
      ID="names", proj4string=CRS("+proj=utm +zone=30 +units=km"))
    eire.nb <- poly2nb(eire)
    #data(eire)
    summary(eire$A)
    brks <- round(fivenum(eire$A), digits=2)
    cols <- rev(heat.colors(4))
    plot(eire, col=cols[findInterval(eire$A, brks, all.inside=TRUE)])
    title(main="Percentage with blood group A in Eire")
    legend(x=c(-50, 70), y=c(6120, 6050), leglabs(brks), fill=cols, bty="n")
    plot(eire)
    plot(eire.nb, coordinates(eire), add=TRUE)
    lA <- lag.listw(nb2listw(eire.nb), eire$A)
    summary(lA)
    moran.test(eire$A, nb2listw(eire.nb))
    geary.test(eire$A, nb2listw(eire.nb))
    cor(lA, eire$A)
    moran.plot(eire$A, nb2listw(eire.nb),
     labels=eire$names)
    A.lm <- lm(A ~ towns + pale, data=eire)
    summary(A.lm)
    res <- residuals(A.lm)
    brks <- c(min(res),-2,-1,0,1,2,max(res))
    cols <- rev(cm.colors(6))
    plot(eire, col=cols[findInterval(res, brks, all.inside=TRUE)])
    title(main="Regression residuals")
    legend(x=c(-50, 70), y=c(6120, 6050), legend=leglabs(brks), fill=cols,
      bty="n")
    lm.morantest(A.lm, nb2listw(eire.nb))
    lm.morantest.sad(A.lm, nb2listw(eire.nb))
    lm.LMtests(A.lm, nb2listw(eire.nb), test="LMerr")
    brks <- round(fivenum(eire$OWNCONS), digits=2)
    cols <- grey(4:1/5)
    plot(eire, col=cols[findInterval(eire$OWNCONS, brks, all.inside=TRUE)])
    title(main="Percentage own consumption of agricultural produce")
    legend(x=c(-50, 70), y=c(6120, 6050), legend=leglabs(brks),
      fill=cols, bty="n")
    moran.plot(eire$OWNCONS, nb2listw(eire.nb))
    moran.test(eire$OWNCONS, nb2listw(eire.nb))
    e.lm <- lm(OWNCONS ~ ROADACC, data=eire)
    res <- residuals(e.lm)
    brks <- c(min(res),-2,-1,0,1,2,max(res))
    cols <- rev(cm.colors(6))
    plot(eire, col=cols[findInterval(res, brks, all.inside=TRUE)])
    title(main="Regression residuals")
    legend(x=c(-50, 70), y=c(6120, 6050), legend=leglabs(brks), fill=cm.colors(6),
      bty="n")
    lm.morantest(e.lm, nb2listw(eire.nb))
    lm.morantest.sad(e.lm, nb2listw(eire.nb))
    lm.LMtests(e.lm, nb2listw(eire.nb), test="LMerr")
    print(localmoran.sad(e.lm, eire.nb, select=1:length(slot(eire, "polygons"))))

[Package spdep version 0.4-56 Index]