similar to: New package: DCluster

Hi, I have just put the source code (and a zip file for Window$ users) of package DCluster in my website: http://matheron.estadi.uv.es/~virgil/Rpackages/DCluster/ DCluster is a package that contains routines for the detection of spatial clusters of diseases (Openshaw's GAM, Besag and Newell, Kulldorff and Nagarwalla, Stone's Test and others). A full description can be found here:

Hi, As Frank M. Howell noticed (and probably other users), the Windows binaries for DCluster I put in my web page are not working... I have compiled the source code again and know it does. Please, download it again, and sorry for the inconvenience. The URL is http://matheron.uv.es/~virgil/Rpackages/DCluster DCluster is a package that implements some methods for the detection of spatial

When I try to load the DCluster package I get the following error message (R 2.5.1; PowerBook G4; Mac OS X 10.4.10) (Load needed package) Lade nötiges Paket: spdep (error: package spdep could not been loaded) Fehler: Paket 'spdep' konnte nicht geladen werden (additional warning message) Zusätzlich: Warning message: (There is no package spdep in:.....) es gibt kein Paket 'spdep'

Hello R-List I use DClusters package (I work in a cancer regestry). I have 2 questions about it: 1-how is it possible to get back the bootstrap pValue? I mean the pValue of the calculated statistic with respect of the distribution of this statistic under the null hypothesis. 2-how is it possible to test an overdispersion in the poisson model? for choosing a best model I need this mesure of

Dear all, I am basically a GIS user and am new to R. I am trying to write a data frame to a dbf file. *n.simulations <- 999 binomial <- kulldorff(geo, cases, population, NULL, pop.upper.bound, n.simulations, alpha.level, plot) cluster <- binomial$most.likely.cluster$location.IDs.included df <- data.frame(ID=seq(1,n.simulations,by=1), simloglkhd=binomial$simulated.log.lkhd)

I referred in my reply to a paper by Diaconis and Sturmfels. The exact reference is: Diaconis and Sturmfels, Algebraic algorithms for sampling from conditional distributions, Ann. Stat 26 (1998) 363-397. They cite the following: Besag and Clifford, Generalized Monte Carlo significance tests, Biometrika 76 (1989) 633-42. which actually contains your problem (section 3, Testing the Rasch model)

I am using rw0641. In my continuing quest to understand repeated measures analysis, I again return to lme. I exported the Potthoff and Roy data Orthodont.dat from S-PLUS 4.5 to avoid capture errors and ran the examples in the R help. I imported the data.frame with data <- read.table("Orthodont.dat",header=T) attach(data) and created the objects Orthodont.fit1 <-

Is there a painless way to find the names of all packages on CRAN that "Depend" on a specified package? url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker at uiuc.edu Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Champaign, IL 61820

Dear Users: I have been trying to use the geary.test() function in *R*, but am having slight difficulty understanding how I am to apply it in my context. I have 2 matrices: 1) *n x p* matrix of *n* observations with *p* measurements each. It may be noted that this matrix has a spatial dimension to it, as the *n*observations are at different geographical locations on a map. 2) *n x n* spatial

Hello, I'm a student in biology, writting from Strasbourg, France. I need to use the logiciel R to analyse biological results. I have ADE4 since 2001, but technology's now largely evoluted ! That's why I'm just trying to get the new version of : - ADE4_1.3-3.zip ; - R 2.0.1.ter ; - DCluster_0.1-3.zip. However, I don't know HOW TO INSTALL R on my computer ? I don't

Hi everybody, I'm performing a cluster analysis (pkg "cluster") on a dataset which includes 15 variables: is there a way to know how much each variable weighs on the final clustering output? Thanks Alessandro

Howdy All, I am looking for some good tutorials (books, websites, whatever) for calculating/testing for Spatial Autocorrelation using R. Specifically, I am wanting to test for autocorrelation of a number of variables measured at a set of discrete locations. Up to this point I have been exploring the "spdep" package and I can get "moran.test" to work, but I am concerned that

Hello List, I'm a very new user to the R system. I'm only beginning to learn the basics, but so far I've been able to do little more than try a few examples, and of course begin reading the documentation. My primary motivation for exploring R is the availability of tools like the 'spdep' package for calculating spatial statistics such as Geary's C and Moran's

-----Original Message----- From: r-help-request at stat.math.ethz.ch [mailto:r-help-request at stat.math.ethz.ch] Sent: Tuesday, July 01, 2003 3:09 AM To: r-help at stat.math.ethz.ch Subject: R-help Digest, Vol 5, Issue 1 At 18:48 30/06/2003, Martin Wegmann wrote: >hello, > >I want to do a test for spatial correlation. >I tried it with geary.test() but I don't understand the

Maybe it's time to add it to the LLVM projects list. :) The original annoucement on digitalmars.D.announce and http://www.incasoftware.de/~kamm/projects/index.php/2009/01/09/ldc-09-released/ was: --- The first version of LDC (http://www.dsource.org/projects/ldc), the LLVM based compiler for version one of the D programming language has been released for x86-32 Linux. Get it here:

Dear R users, I have found a quite uncommon behaviour when compiling a library I want to call from R (avce00, in package RArcInfo). I define CPL_LSB (-DCPL_LSB is passed to the compiler in the command line) but it's useless, since #ifdef CPL_LSB ... #endif clauses doesn't notice that CPL_LSB is defined. I also set -O2. Any clue? I have tested the library without optimising the code and

Hi, I have to run lots of simulations based on several spatial scan statistics. Since the main overhead is due to a call to 'apply' , I was wondering whether it could be possible to parallelize this "loop". I know there are a few packages for parallel computing in R but I don't know whether it's worth trying them. Have anyone used these packages before? I have a linux

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

Dear R experts, Attached you can find the expression of a loglikelihood function which I would like to maximize in R. So far, I have done maximization with the combined use of the mathematical programming language AMPL (www.ampl.com) and the solver SNOPT (http://www.sbsi-sol-optimize.com/manuals/SNOPT%20Manual.pdf). With these tools, maximization is carried out in a few seconds. I wonder if that

Dear useRs, I was searching CRAN for implementation of kurtosis and skewness tests, and found that there is some kind of lack on it. So, I have written three functions: 1. Anscombe-Glynn test for kurtosis 2. Bonett-Seier test based on Geary's kurtosis (which is not widely known, but I was inspired by original paper describing it, found coincidentally in Elsevier database) 3.