How to handle NA-values in raster-based Geary´s C test?

Zitat von Roger Bivand <Roger.Bivand at nhh.no>:

On Thu, 12 Jul 2012, Kerstin Traut wrote:

Zitat von Roger Bivand <Roger.Bivand at nhh.no>:

On Thu, 12 Jul 2012, Kerstin Traut wrote:

Zitat von Roger Bivand <Roger.Bivand at nhh.no>:

On Thu, 12 Jul 2012, Roger Bivand wrote:

On Thu, 12 Jul 2012, Kerstin Traut wrote:

Zitat von Roger Bivand <Roger.Bivand at nhh.no>:

On Thu, 12 Jul 2012, Kerstin Traut wrote:

Hi,

I have a question on testing spatial autocorrelation on raster data 
including NA-values. In particular, I like to calculate Moran?s I 
and Geary?s C indices by using inverse distance weighting matrices.
Calculating Moran?s I with moran.test works fine, because the 
function contains the option "na.action=na.pass". Unfortunately, 
the function geary.test does not contain this option. The problem 
is that geary.test needs an nb argument for which I cannot perform 
for example nb<-ifelse(value, nb, NA), because it would change the 
nb-format to a list-format.
Is there a way to ignore NA-values in geary.test? Or how can I set 
NA values in a neigbors (nb) list?

The simple answer is to subset the variable of interest and the nb 
object to omit NA observations (which is what moran.test does 
internally). Alternatively, you could contribute a patch to 
geary.test(), if you like.

Hope this helps,

Roger

Thanks for your answer. Unfortunately, I think that subsetting is in 
my case not the right solution, because I need to define the amount 
of neighbors in cell2nb on the basis of a rectangular extent (e.g. 50 
x 50 neighbors). Subsetting this extent by deleting NA-values would 
destroy the nb list format required by geary.test.
Is there any solution for subsetting an nb list without destroying 
the nb format?

Yes, there are subset methods for nb and listw objects. To ensure data 
integrity, use dnearneigh() instead of cell2nb(), and use a 
SpatialPointsDataFrame to hold your data. In that way, you make sure 
that the subset condition is applied to the same observations.

The latter advice may not work if your coordinates are planar and you 
really mean to set torus=TRUE in cell2nb(). You'll have to get the data 
order right by deconstructing the region.id attribute of the nb object 
created by cell2nb() to ensure data integrity.

Roger

I already tested dnearneigh(), but I like to avoid this function because 
for large datasets cell2nb() calculates neighbors more quickly.
Thanks to your suggestion I was able to subset the nb list by using 
nb.subset(). But I could not find a suitable function for subsetting a 
listw. Using the subset() function results in the following error 
message: "Not yet able to subset general weights lists". Do you have an 
explaination?

Yes, because the changes may not be appropriate, depending on how the 
general weights were defined. In your case, subset the nb object and the 
list of idw weights, and rebuild the listw object with the subsetted 
objects.

Roger

That?s what I already tried, but the error message says "neighbors and 
glist do not conform"??

Right, you'll need to rebuild from the subsetted nb object from the 
nbdists() step (and subsetted coordinates), because the subsetted idw list 
will have weights for excluded observations.

Roger

Ok, I hope I understood everything right. You mean, firstly, I need to subset 
my nb list and afterwards, I need to calculate the nb-distances using 
nbdists()?

Kerstin

Kerstin

Kerstin

Roger

Kind regards,
Kerstin

Thank you very much,
Kerstin

---

file<-raster(test.tif")
mask<-extent(file, 1, 50, 1, 50)
subset<-crop(file,mask,test.tif", overwrite=TRUE)
value <- as.matrix(subset)

# Creation of a list of integer vectors giving the region id 
numbers for the neighbors within the grid extent
nb <- cell2nb(50, 50, type="queen", torus=TRUE)

# Calculation of the distances along the links in the neighbous 
list
dist <- nbdists(nb, coordinates(subset), longlat=TRUE)

# Converting the distances to inverse distance values
inv.dist <- lapply(dist, function (x) 1/(x*100))

# Supplementing the neighbors list with spatial weights for a 
coding scheme
nb.idw <- nb2listw(nb, glist=inv.dist, style="W")
summary(unlist(nb.idw$weights))

# Moran's test (two-sided)
moran.global <- moran.test(value, listw=nb.idw, zero.policy = TRUE, 
na.action=na.pass, alternative="two.sided")
moran.global

# Geary's test (two-sided)
geary.global <- geary.test(value, listw=nb.idw, zero.policy = TRUE 
, alternative="two.sided")
geary.global

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Roger Bivand
Department of Economics, NHH Norwegian School of Economics,
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voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no

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voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no

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voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no

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Department of Economics, NHH Norwegian School of Economics,
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voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: Roger.Bivand at nhh.no

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