Wonderful thank you.

datasets. Thanks for the reprex example with the GUI via ">". That is a

helpful convention I'll use in the future..

Ok great, there is not yet a CI implementation. If I can manage one I'd be

delighted to share and contribute it.

they work together a lot).

including covariates.

> On Fri, 24 Apr 2020, Dexter Locke wrote:

>

> > Thanks Roger and list.

> >

> > I didn't think a repex was needed because a question was: why does

> > spdep::EBest(counts, population, family = 'binomial') give the same

> > results at GeoDa's, while EBest(.. binomial) is "binomial" while GeoDa

> > calls that "Poisson-Gamma". GeoDa can't give use a repex (GUI) and think

> > this is a question about terminology. The same results were achieved with

> > the packages while naming the model differently - why?

>

> Reproducible example:

>

> auckland <- st_read(system.file("shapes/auckland.shp",

> package="spData")[1], quiet=TRUE)

> res <- EBest(auckland$M77_85, 9*auckland$Und5_81)

> res0 <- EBest(auckland$M77_85, 9*auckland$Und5_81, family="binomial")

>

> > summary(res$estmm)

> Min. 1st Qu. Median Mean 3rd Qu. Max.

> 0.001487 0.002237 0.002549 0.002648 0.002968 0.004531

> > summary(res0$estmm)

> Min. 1st Qu. Median Mean 3rd Qu. Max.

> 0.001484 0.002235 0.002549 0.002648 0.002968 0.004536

>

> After calculating Und5_81 * 9 as a new column, running GeoDa -> Map ->

> Rates-Caluculated Map -> Empirical Bayes and exporting a shapefile, in R I

> see:

>

> > summary(auck$R_EBS)

> Min. 1st Qu. Median Mean 3rd Qu. Max.

> 0.001487 0.002237 0.002549 0.002648 0.002968 0.004531

>

> which is the same as the Poisson, and:

>

> > all.equal(res$estmm, auck$R_EBS)

> [1] TRUE

>

> GeoDa is providing the same EB Poisson as EBest, isn't it? If yours

> differ, are both implementations seeing the same data?

>

> >

> > Yes ?spdep::EBest directed me to the literature I'm struggling to access.

> > And Yes, I've been looking at the raw code and understand how the estmm

> is

> > generated.

> >

> > I've been using the epitools::pois.exact() and spdep::EBest. I can

> compare

> > the point estimates from pois.exact to those provided by EBest, but I'd

> > like to graph side by side their credible / confidence intervals.

> >

> > Its this last part on the credible intervals I'm interested in. How to

> get

> > credible intervals around estmm? This is my main question.

>

> EBest() was written to implement Bailey & Gatrell's textbook, which did

> not provide CI, and just used the Marshall Auckland dataset. If you'd like

> to implement them, I'd welcome a contribution.

>

> >

> > ASDAR is a reference I'm using all the time. Thanks for that gem.

> >

> > DCluster::empbaysmooth also does not provide a credible interval, either.

> >

>

> As you see from ch. 10, CI are described for the epitools implementation.

> My feeling is that the literature moved away from simple EB rates towards

> IID RE and spatially structured RE, with relevant covariates, say like the

> classic Scottish Lip Cancer dataset, and currently CARBayes is a solid

> package among many others. Simply using base population becomes too

> unsatisfactory. PHE uses funnel plots which do have CI of a kind, to draw

> attention from small base populations:

>

https://nhsrcommunity.com/blog/introduction-to-funnel-plots/ which can be

> used to adjust class intervals for mapping.

>

> Roger

>

> > -Dexter

> >

http://dexterlocke.com/> >

> >

> >

> > On Fri, Apr 24, 2020 at 10:23 AM Roger Bivand <

[hidden email]>

> wrote:

> >

> >> On Fri, 24 Apr 2020, Dexter Locke wrote:

> >>

> >>> Dear esteemed list,

> >>>

> >>> I'm using spdep::EBest with family = 'binomial' for counts of events

> >> within

> >>> polygons that have an 'at risk' population. The resultant "estmm" is

> >>> 'shrunk' compared to the raw rate (both given by EBest and calculated

> "by

> >>> hand" rate. All good there.

> >>>

> >>> Using GeoDa version 1.14.0 24 August 2019 produces identical results

> for

> >>> its Empirical Bayesian rate. This was confirmed by plotting the EBest

> >>> output against GeoDa's rate and finding a perfect correlation along

> the 1

> >>> to 1 line. All good there.

> >>

> >> Please provide a reproducible example, as this may help with answers.

> >>

> >>>

> >>> Two questions:

> >>> 1. How can credible intervals around these smoothed rate estimates be

> >>> calculated?

> >>> 2. The spdep documentation calls this a binomial family, but the

> >> identical

> >>> results are obtained from GeoDa calls this "Poisson-Gamma" model here:

> >>>

https://geodacenter.github.io/workbook/3b_rates/lab3b.html#fnref11 ,

> so

> >>> what is actually being calculated? This question may help me answer the

> >>> first question..

> >>

> >> No, the default family is "poisson", with "binomial" available for

> >> non-rare conditions following Martuzzi, implemented by Olaf

> >> Berke, ?spdep::EBest.

> >>

> >> The code in spdep is easily accessible, so can be read directly. Please

> >> also compare with code for the EB Moran test, and with analogous code in

> >> the DCluster package, empbaysmooth(). Cf. ASDAR 2nd ed., ch. 10, section

> >> 10.2, pp. 322-328. The epitools::pois.exact() function is used for CIs.

> >> For code and data see

>

https://asdar-book.org/bundles2ed/dismap_bundle.zip.

> >>

> >>>

> >>> Possibly the answers are addressed in the literature cited which I

> cannot

> >>> access right now at home without institutional library access.

> >>>

> >>

> >> Most institutions do have proxy or VPN access, but the code will be as

> >> useful. In PySAL, the code would also guide you, but even though GeoDa

> is

> >> open source, the C++ is fairly dense.

> >>

> >> Hope this helps,

> >>

> >> Roger

> >>

> >>> Thanks for your consideration,

> >>> Dexter

> >>>

http://dexterlocke.com/> >>>

> >>> [[alternative HTML version deleted]]

> >>>

> >>> _______________________________________________

> >>> R-sig-Geo mailing list

> >>>

[hidden email]
> >>>

https://stat.ethz.ch/mailman/listinfo/r-sig-geo> >>>

> >>

> >> --

> >> Roger Bivand

> >> Department of Economics, Norwegian School of Economics,

> >> Helleveien 30, N-5045 Bergen, Norway.

> >> voice: +47 55 95 93 55; e-mail:

[hidden email]
> >>

https://orcid.org/0000-0003-2392-6140> >>

https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en> >>

> >

>

> --

> Roger Bivand

> Department of Economics, Norwegian School of Economics,

> Helleveien 30, N-5045 Bergen, Norway.

> voice: +47 55 95 93 55; e-mail:

[hidden email]
>

https://orcid.org/0000-0003-2392-6140>

https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>