similar to: Question: nonlinear covariate terms in spatial regression

Hi all, I am wondering if anyone has written (or knows of) a function that will conduct a goodness-of-fit test for a gamma distribution. I am especially interested in test statistics have some asymptotic parametric distribution that is independent of sample size or values of fitted parameters (e.g., a chi-squared distribution with some fixed df), because I want to fit gamma distributions to

Hi. I'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)

Hello, Sorry to be a bit longwinded, but I've struggled quite a bit with the following over the last few days. I've read all entries related to spatial autocorrelation in R help and haven't found what I'm after. If it's okay, I'm going to first describe my general understanding of the process by which a mixed model can account for correlated errors. If possible, please

I have a complicated nonlinear function, myfun(a,b,c), that I want to fit to data, allowing one or more of the parameters a, b, and c in turn to have linear dependence on other covariates. In other words, I'd like to specify something like nls(y~myfun(a,b,c),linear=list(a~f1,b~1,c~1)) I know would this work in nlme *if I wanted to specify random effects as well*, but I don't -- and

Dear R users: I want to model mosquito count data based on landcover attributes and meteorological variables using a Poisson GLSM in the geoRglm package. I have monthly mosquito counts over more than 20 years with repeated observations from individual trap sites over time. I have used as.geodata() to successfully read my dataset into the geodata format utilized by geoR and geoRglm,

Hello, I recently started using the metafor package (version 1.6-0) in R (2.15.1, 64-bit Windows 7) and noticed that I was getting slightly different values when I manually calculated the standardized mean difference versus what escalc was giving me. Here''s a very simple example: escalc(measure="SMD", m1i=5,m2i=10,n1i=5,n2i=5,sd1i=1,sd2i=2,vtype="LS") The result

Dear list, I have two dissimilarity matrices, one for a training data set which I then clustered using PAM. The second is a diss matrix for a validation data set (an independent field sample). I have been trying to use knn to distinguish distances between the validation data set and the 6 mediods of the training data defined by using PAM. I continue to get error messages in regards to either the

Dear David, you asked this question a while ago on the R mailing list and got no answer. I have the same problem and was wondering if you had found a solution Cheers Loic Loic Thibaut, PhD candidate, ARC Centre of Excellence for Coral Reef Studies, School of Marine Biology, James Cook University, Townsville, Qld, 4811, Australia. Tel + 61 747 815 735, Fax: + 61 747 251 570, email:

Hi I am experiencing a problem with the random number generator. When I call any function that involve RNG such as "runif" or "sample" I get this error: .Random.seed is not an integer vector but of type 'double' I can't coerce the random seed and it's recommended not to alter it anyway. Can anybody help me? Thanks and kind regards, Piero Visconti, PhD

Hello List, My question is an elementary one. I have run a fuzzy kmeans cluster using FANNY to group freshwater fish assemblages. I then went in the field to validate that classification and have retrieved new assemblage data for a new suite of streams. Therefore I would like to use Predict to determine how well the original clustering fits the new data. However I have not figured out a

Hello, I install R under Mandrake Linux 8.2. R itself work fine, but I had an error to install packages, i.e. fields, geoR and geoRglm : $: R CMD INSTALL geoRglm_0.4-3.tar.gz Installing *source* package `geoRglm' ... libs gcc-3.0.1 -I/usr/lib/R/include -I/usr/local/include -mieee-fp -D__NO_MATH_INLINES -fPIC -O3 -fomit-frame-pointer -pipe -mcpu=pentiumpro -march=i586 -fno-fast-math

Hello, I install R under Mandrake Linux 8.2. R itself work fine, but I had an error to install packages, i.e. fields, geoR and geoRglm : $: R CMD INSTALL geoRglm_0.4-3.tar.gz Installing *source* package `geoRglm' ... libs gcc-3.0.1 -I/usr/lib/R/include -I/usr/local/include -mieee-fp -D__NO_MATH_INLINES -fPIC -O3 -fomit-frame-pointer -pipe -mcpu=pentiumpro -march=i586 -fno-fast-math

Hi, I am stuck in a looping problem. It might be an easy problem for experienced R users but I have been unable to do it. Any kind of help or advice will be great appreciated. I am creating a spatial grid and have a list of spatial points in a folder. I can read the spatial points separately and get a count of the points in the spatial grid in R. The output should be a table showing the counts of

We are pleased to announce the following book: Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA Authors: Zuur, Ieno, Saveliev Book website: www.highstat.com Paperback or EBook can be order (exclusively) from www.highstat.com TOC: http://highstat.com/Books/BGS/SpatialTemp/Zuuretal2017_TOCOnline.pdf Summary: We explain how to apply linear regression models,

Dear R users, I am putting together reading and resources lists for spatial statistics and spatial econometrics and am looking for some pointers from more experienced practitioners. In particular, I find two "camps" in spatial modelling, and am wondering which approach is better suitied to which situation. The first camp is along the lines of Venables and Ripley's Chapter 14

Dear R users. I'm trying to fit a generalised linear spatial mode using the geoRglm package. To do so, I'm preparing my data (geodata) as follow: geoData9093 = as.geodata(data9093, coords.col= 17:18, data.col=15,* covar.col=16*) where covar.col is a factor variable (years in this case 90-91-92-93)). Then I run the model as follow: / model.5 = list(cov.pars=c(1,1),

Something I can't quite place finger on here. Altogether too much Mafia, in the bulk email business generally, and I know Switzerland borders on Italy ... This sounds, (albeit vaguely,) altogether too much like the thieves I seem to have fallen amongst lately. Two stolen trucks, three stolen laptops, another one wrecked, three or four stolen cell phones, passwords GPG keys, city hall hookers

I am attempting to analyse some behaviour sequence data. The input is an alphabetic string "ASDFGH ... ". I wish to start at one end of the string, peel off each character, and convert to an integer to develop transition matrices etc. My blundering through the ref manual hasn't produced any light. Can this be done easily in R or should I pre-process? I recollect an item

Hello R folks, I'm hoping the answer to the question in the subject line. I have in the past used SAS PROC NLIN and PROC NLP to carry out nonlinear optimizations. I'm wondering if there is analogous ways for doing this using R. If so, could someone please point me to some literature that would help me examine this further? Thanks very much. [[alternative HTML version deleted]]

Hi guys I have data that contains the variances vt of the yields of 1, 2, 3, 4, 5,10, 20 year bonds. Assuming the Hull-White model for the yield of a t-year zero-coupon bond, I have to estimate the ? of the Hull-White model using nonlinear least squares and give a 95% con?dence interval for each parameter. Please can you guys tell how to find out ? using R. Any suggestion regarding what functions