DCluster

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DCluster

Jakob Petersen-2
Hi r-sig-geo,
I am looking at the spatial distribution of poor households in a region
comprising a gradient of urban-rural postcodes. The data are counts and
they fit a negative binomial distribution, rather than a poisson
distribution.

I am applying DCluster (ver. 0.1-3, windows) and would be grateful for
advice on a few topics.

1. GAM

A) As I understand it the default setting is based on a poisson
distribution. This creates some not implausible clusters, but I wonder
whether I could set the opgam, so that it uses a negative binomial
distribution (for which I have the parameters for the ?disease?
variable; size and mu) or to use a bootstrap procedure instead. Some of
the internal functions, like opgam.iscluster.negbin, seem to support
this, but I am uncertain about how to incorporate them.

B) To reduce the multiple testing problem (Waller & Gotway 2004,
?Applied Spatial Statistics for Public Health Data?, Wiley, p.208) I
wonder whether to set radius to <50% of step size, e.g. 100m radius in a
300m grid, so that the smallest circles won't touch?

2. Besag-Newell

I am getting results with ?poisson? (almost everything becomes a cluster
- possibly because the sites are clumped and not randomly distributed)
and with ?permutation?, but wonders how the ?negbin? is used? Not like
this:

>  bnresults<-opgam(pcpoor, thegrid=pcpoor[,c("x","y")], alpha=.05,

+ iscluster=bn.iscluster, set.idxorder=TRUE, k=20, model="negbin",

+ R=100, mle=calculate.mle(pcpoor) )

> > Error in rnbinom(n, size, prob) : invalid arguments

3. Kulldorff & Nagarwalla

Again I struggle with the parameters. Not like this:

>  #K&N's method over the centroids

>  mle<-calculate.mle(pcpoor, model="negbin")

> > Error in while (((abs(m - m0) > tol * (m + m0)) || (abs(v - v0) > tol
* :

missing value where TRUE/FALSE needed

>  knresults<-opgam(data=pcpoor, thegrid=pcpoor[,c("x","y")], alpha=.05,

+ iscluster=kn.iscluster, fractpop=.5, R=100, model="negbin", mle=mle)

> > Error in rnbinom(n, size, prob) : invalid arguments

4. Turnbull. Is Turnbull analysis possible in DCluster yet?. Some
references in the manual, but haven?t been able to locate it.

5. General

A) I am considering increasing the study area (p.t. working with 1262
postcode points) and wonder what the limits might be for a desktop pc. I
gather that the distance matrices (created by tripack or spdep) could be
a limiting factor? Would it be an idea to run this step first and once
the table is created run the cluster detection algorithm?

B) I wonder whether permutations always are superior to standard stats.
Distributions, and if not, then why not?


Best wishes, Jakob

Jakob Petersen
GISc student (MSc)
Birkbeck, University of London



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Re: DCluster

Virgilio Gómez Rubio
Hi all,

I am out of my office until next week. For this readon, I will not be
able to check the code in DCluster and answer your other question,
Jakob. Don't desperate!! :)

The only advice I can give yiou a the moment is to try to follow as
much as possible the code in the examples, although I am quite sure
that you have already done it. I remember some problems like those
that you described but I think I fixed them (or maybe not) for the
current release.

Best regards,

Virgilio