similar to: ordinal response model with spatial autocorrelation

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

I have a spatial data set with ordinal response variable containing four levels. I would like to know if and how spatial autocorrelation can be taken into account when ordinal logistic regression is used (e.g. the function lrm from the Design package). Thanks for your help! Christof

I tried thinning of the mcmc run with 500,000 iteration. It looks like 100 or 200 is enough to remove the autocorrelation of a1 and tau. Is that too much thining? --- On Tue, 3/16/10, ping chen <chen1984612 at yahoo.com.cn> wrote: > From: ping chen <chen1984612 at yahoo.com.cn> > Subject: an ordinal regression MCMC run high correlation > To: r-help at r-project.org >

Hello, I have a matrix with the distances among sites. And I have another matrix with the presence and absence of each species in each site. I would like to test the spatial autocorrelation among sites. I have tried to use the function gearymoran of the ade4 package, but error messages keep popping up. Do you know any function for me to test the spatial autocorrelation of my data? Thanks,

I am trying to model a clusterd ordinal response data (either 1, 2 or 3) called , the correponding physician of the patient is also in the data. Since it is ordinal, I used the ordinal logit model topbox[i]~discrete with probability P[j,1],p[j,2], p[j,3], j is the corresponding physician of the ith patient C[j] is the physician effect , a1 and a1+theta is the common cutpoints for all

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 folks, I have some data where spatial autocorrelation seems to be a serious problem, and I'm unclear on how to deal with it in R. I've tried to do my homework - read through 'The R Book,' use the online help in R, search the internet, etc. - and I still have some unanswered questions. I'd greatly appreciate any help you could offer. The super-super short explanation is

Dear all, I am currently facing a problem related to the spatial autocorrelation of a sample of stations; these stations supply weekly data for a fixed time-window during the year (namely, 4-6 months per year). For this reason I'm trying to use the R package 'spdep' (specifically Moran's I) in order to get rid of it. Does anyone know how is it possible (if it is...) to

Dear all: Does anyone have any suggestions on how to make a spatially explicit landscape with spatial autocorrelation in R? In other words, a landscape where all cells have a spatial reference, and the environment values that are closer in space are more similar (positive spatial autocorrelation). Thank you, Laura

Dear all: I would like to create a landscape of environmental values that follow a uniform frequency distribution and also have spatial autocorrelation in the landscape. I was wondering if there is an algorithm and/or package out there that creates autocorrelation of values that are distributed according to a non-normal frequency distribution. Any suggestions are greatly appreciated. Thank you,

Hi all, I am analyzing data on habitat utilization of seals in the Southern Ocean. My data show spatial autocorrelation, which I'm interested in incorporating into my model. I am trying to model the presence of dives (versus simulated pseudo-absences) using a binomial generalized binomial model (glmmPQL), since I can incorporate the autocorrelation structure to the model using that package.

Hi R helpers, Is there a function (package?) in R available which tests "spatial autocorrelation" between points (e.g. vector layer of weather stations)? (e.g. Moran's I...) Via the archives we found out that there is a package 'spdep' which uses grid data for testing spatial autocorrelation. Thanks a lot, Jan

Dear all, I am experiencing problems using the glmmPQL function in the MASS package (Venables & Ripley 2002) to model binomial data with spatial autocorrelation. My question - is the presence of birds affected by various hydrological parameters? Presence/absence data were collected from 83 sites and coupled against hydrological data from the same site. The bird survey sampling effort

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

Hi, I have two global raster maps, each of the same variable but from different sources. The values range from 0 to 5 in whole numbers. Is there a statistical test in R that can quantify the similarity of the spatial patterns (i.e., highs and lows)? Thanks, -- View this message in context: http://r.789695.n4.nabble.com/map-similarity-spatial-autocorrelation-in-R-tp4647877.html Sent from the

Dear all, I collected my data from the different agricultural fields every week over a period of a month. how can I test for spatial autocorrelation in R with data that are temporally pseudoreplicated? I used lme with correlation=corCompSymm(form=~Date) to model temporal pseudoreplication. Regards, VG

Hi, I have collected data on trees from 5 forest plots located within the same landscape. Data within the plots are spatially autocorrelated (calculated using Moran's I). I would like to do a ANCOVA type of analysis combining these five plots, but the assumption that there is no autocorrelation in the residuals is obviously violated. Does anyone have any ideas how to incorporate these spatial

Hello all, This may be a simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas with unbalanced design in R. In my case, I have 11 vegetation transects (with 1000 10cmx10cm squares), where we estimated shrub cover. We have two different treatments: wildfire (4 transects) and prescribed burning (7 transects) and we want to

I have been asked to analyse some questionnaire data- which is not data I'm that used to dealing with. I'm hoping that I can make use of the nabble expertise (again). The questionnaire has a section which contains a particular issue and then questions which are related to this issue (and potentially to each other): 1) importance of the issue (7 ordinal categories from -3 to +3) 2) impact