A simple correlogram using sp.correlogram with data(meuse)

Thanks Roger! That solves it.
Best,
Mike

On Tue, Apr 28, 2015 at 12:49 PM, Roger Bivand <Roger.Bivand at nhh.no>
wrote:

On Tue, 28 Apr 2015, Michael Treglia wrote:

 Hi all,

This is a bit a tangential to the question, but related to
pgirmess::correlog used in this thread, so I thought it appropriate to
follow-up here [if not, apologies - I can start a new thread if that's
preferred].

In recent exploration with the pgirmess::correlog, I was unable to change
the alternative hypothesis being tested. Since pgirmess::correlog uses
spdep::moran.test, the alternative hypothesis being tested is by default
"greater" (confirmed in the documentation). The help for the function
indicates pgirmess::correlog should also be able to take arguments from
'moran.test' within it. Thus, users should be able to use the
'alternative'
argument to change the alternative hypothesis being tested.  When trying
to
use "alternative='less'" (or 'two.sided'), an error is returned:

Error in try(moran.test(lst.z1[[i]], nb2listw(lst.nb1[[i]], style =
"W")),
:
 unused argument (alternative = "less")

The problem is that the ... is passed to try(), not moran.test() or
geary.test() in:

xt <- switch(pmatch(method, c("Moran", "Geary"), nomatch = 3),
    try(moran.test(lst.z1[[i]], nb2listw(lst.nb1[[i]],
        style = "W")), silent = TRUE, ...), try(geary.test(lst.z1[[i]],
        nb2listw(lst.nb1[[i]], style = "W")), silent = TRUE,
        ...), stop("Method must be 'Moran' or 'Geary'"))

With:

xt <- switch(pmatch(method, c("Moran", "Geary"), nomatch = 3),
    try(moran.test(lst.z1[[i]], nb2listw(lst.nb1[[i]],
        style = "W"), ...), silent = TRUE), try(geary.test(lst.z1[[i]],
        nb2listw(lst.nb1[[i]], style = "W"), ...), silent = TRUE),
        stop("Method must be 'Moran' or 'Geary'"))

via for example correlog1 <- edit(correlog), it works. You can save the
function for later use too.

Hope this helps,

Roger

Reproducible Code (based on the example from help for
pgirmess::correlog):

library(spdep)
library(pgirmess)
data(oldcol)
attach(COL.OLD)
coords<-cbind(X,Y)
res<-correlog(coords,CRIME, method="Moran",
nbclass=NULL,alternative="less")
## Error in try(moran.test(lst.z1[[i]], nb2listw(lst.nb1[[i]], style =
"W")),  :
##  unused argument (alternative = "less")

I corresponded with the author of pgirmess and he did not have a solution
nor much time to work on it. I was hoping to give a go at
trouble-shooting
myself, but haven't had a chance, so this thread seemed like a relevant
place to bring up the issue, so others are at least aware of it.

Best,
Mike Treglia






On Tue, Apr 28, 2015 at 2:05 AM, Roger Bivand <Roger.Bivand at nhh.no>
wrote:

 On Tue, 28 Apr 2015, Andy Bunn wrote:

 Hi all, This seems trivial easy but being a bear of very little
brain...

I'm looking to build a correlogram using Moran's I and the function
sp.correlogram() with data(meuse) as a SpatialPointsDataFrame. I've
done
so with the ncf library using correlog() but getting the equivalent
out of
library sp is eluding me. Can somebody help me out?

require(ncf)
ncf.cor <- ncf::correlog(x=coordinates(meuse)[,1],
 y=coordinates(meuse)[,2], z=meuse$zinc, increment=200, resamp=100)

ncf::correlog() chooses bins based on increment. Alternatively,
pgirmess::correlog() uses by default an optimal number of bins, but can
use
a bin count:

library(pgirmess)
pgir.cor <- pgirmess::correlog(coordinates(meuse), z=meuse$zinc,
"Moran",
  nbclass=23)
plot(pgir.cor[,1], pgir.cor[,2], type="b")
cbind(pgir.cor[,1], ncf.cor$mean.of.class)
cbind(pgir.cor[,4], ncf.cor$n)

The differences come from ncf::correlog() constructing the spatial
weights
itself, while pgirmess::correlog() uses spdep::dnearneigh() to construct
them for the distance bins and then spdep::nb2listw() and
spdep::moran.test() internally in a loop, once for each bin.

spdep::sp.correlogram() follows the original Cliff & Ord understanding
of
a spatial correlogram - like time series lags - of order 1, order 2,
etc.
The input spatial weights define the weighted graph, so order 2
neighbours
are neighbours of order 1 neighbours by stepping out on the graph.
pgirmess::correlog() was written to use distance rather than graph edge
count distance:

library(spdep)
nb200 <- dnearneigh(meuse, 0, 200)
spdep.cor <- spdep::sp.correlogram(nb200, meuse$zinc, order=23,
 method="I", zero.policy=TRUE)
plot(spdep.cor)

To get the implied mean distances for lags of nb200:

nb200_23 <- nblag(nb200, 23)
mdbins <- sapply(nb200_23, function(x) mean(unlist(nbdists(x, meuse))))
mdbins

There are two different understandings of this spatial correlogram, the
C&O one based on time series and counting distance in graph edges, and
another using distance bands, also found in globalG.test, etc.

Hope this clarifies,

Roger



 -Andy

require(sp)
require(ncf)
data(meuse)
coordinates(meuse) <- ~x+y
proj4string(meuse) <- CRS("+init=epsg:28992")
ncf.cor <- correlog(x=coordinates(meuse)[,1], y=coordinates(meuse)[,2],
z=meuse at data$zinc, increment=200, resamp=100)

Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 91 00
e-mail: Roger.Bivand at nhh.no

--
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 91 00
e-mail: Roger.Bivand at nhh.no