similar to: autologistic modelling in R

Hello everyone, I have some binary, spatially autocorrelated data I would like to run autologistic regression on. I hope to incorporate both ordinary covariates (environmental predictors) and a spatial autocovariate in the model, ideally with a second-order neighbourhood structure. Since my computing skills are limited, I am wondering if anyone has composed an algorithm for this purpose, and

Dear all, Could you pleas advise me on the following? I need to use general(ized) linear models (binomial distribution + logit link function) , to describe the preferred environment of each species (each sample is an individual in which I have measured several variables and also recorded the species it belongs to) However, must account for the spatial autrefoocorrelation between

Hi, I am new to autologistic regression and R. I do have questions when starting a project in which I believe autologistic regression is needed. I have a point layer whose attribute table stores the values of the dependent variable and all the independent variables. I hope to to fit an autologistic model to analyze which factors or combinations of factors have effects on the presence/absence of

Dear R users: is there a package, similar to varclus in SAS or varclus in S, ported or written for R? Also, is there any other package in R that was designed for grouping the variables under different measures of distance (in cases where data is non-Gaussian, autocorrelated, and so on). Janusz. -- ** Janusz Kawczak ** ** UNC at Charlotte,

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

Hi, I have two sets of data for a given set of (non-lattice) locations. I would like to know whether the two are significantly different. This would be simple enough if it wasn't for the fact that the data is spatially autocorrelated. I have come across several possible solutions (including Cliff & Ord which however appears to be for gridded data), or using gls. However, they don't

Hi there, Just wondering why my post was rejected? cheersRachel Subject: repeating autocovariate functions From: r-help-owner@r-project.org To: moyble@hotmail.com Date: Sat, 2 Feb 2013 02:56:27 +0100 Message rejected by filter rule match --Forwarded Message Attachment-- Date: Fri, 1 Feb 2013 17:56:14 -0800 From: moyble@hotmail.com To: r-help@r-project.org Subject: repeating autocovariate

Dear all, Sorry for such basic question, but. when R "says": library(Rcitrus) Loading required package: geoR Loading required package: sp ------------------------------------------------------------- Analysis of geostatistical data For an Introduction to geoR go to http://www.est.ufpr.br/geoR geoR version 1.6-8 (built on 2006/06/29) is now loaded

Hello List, I am not sure that I have the correct terminology here (restricted bootstrap) which may be hampering my archive searches. I have quite a large spatially autocorrelated data set. I have xy coordinates and the corresponding pairwise distance matrix (metres) for each row. I would like to randomly sample some number of rows but restricting samples such that the distance between them is

Hello, I am attending a course in Computational Statistics at ETH and in one of the assignments I am asked to prove that a time series is not autocorrelated using the R function "acf". I tried out the acf function with the given data, according to what I found here: http://landshape.org/enm/options-for-acf-in-r/ this test data does not look IID but rather shows some trends so how can I

Hi, I would like to specify a spherical correlation structure for spatially autocorrelated residuals in a model based upon the logistic function of a response that is a proportion (0 to 1) (so usual binary logistic regression is not an option). There is no need for a g-side random effect with grouping in this model. Am I correct that nlme requires this (meaning a correlated error structure only

Hi R-fellows, I am trying to simulate a multivariate correlated sample via the Gaussian copula method. One variable is a binary variable, that should be autocorrelated. The autocorrelation should be rho = 0.2. Furthermore, the overall probability to get either outcome of the binary variable should be 0.5. Below you can see the R code (I use for simplicity a diagonal matrix in rmvnorm even if it

Hi Everyone, I am trying to do a time series regression using the lm function. However, according to the durbin watson test the errors are autocorrelated. And then I tried to use the gls function to accomodate for the autocorrelated errors. My question is how do I know what ARMA process (order) to use in the gls function? Or is there any other way to do the time series regression in R? I highly

Hi, I have fitted a model using a glm() approach and using a gls() approach (but without correcting for spatially autocorrelated errors). I have noticed that although these models are the same (as they should be), the AIC value differs between glm() and gls(). Can anyone tell me why they differ? Thanks, Geertje ~~~~ Geertje van der Heijden PhD student Tropical Ecology School of Geography

Hi there, I am using gls from the nlme library to fit an AR(1) regression model. I am wondering if (and how) I can separate the auto-correlated and random components of the residuals? Id like to be able to plot the fitted values + the autocorrelated error (i.e. phi * resid(t-1)), to compare with the observed values. I am also wondering how I might go about calculating confidence (or

Hi, I have two time series, y and x. Diff(y) and Diff(x) both show no autocorrelation. But durbin.watson(lm(Diff(y)~lag(Diff(x),k=-4)) gives a DW value of zero. How come the residule is autocorrelated while Diff(y) and Diff(x) are not? Does anyone know if in my case a DW of zero indicates serial correlation, or is it telling me that the DW statistics is not the appropriate statistics to use here?

CRAN (and crantastic) updates this week New packages ------------ Updated packages ---------------- New reviews ----------- This email provided as a service for the R community by http://crantastic.org. Like it? Hate it? Please let us know: cranatic at gmail.com.

Dear R users, I am using spatstat to analyze point patterns (tree locations). I would like to import the shapefile with the study area polygons (six total) into R and use it to create the window for the spatstat analysis. I do not simply want to use a rectangle because the study areas spread out over 40000 ha. Any suggestions would be greatly appreciated. Thanks, Charlotte Reemts Charlotte

dear R users, I have troubles performing Moran.I test as suggested on http://www.ats.ucla.edu/stat/r/faq/morans_i.htm my spatial data are longitude and lattitide of communities. The calculation of the inverse distance matrix according to the homepage (using my data) datAL <- read.csv2("C:\\Konvergenz AL.csv", header=T) ALdist <- as.matrix(dist(cbind(datAL$L?nge,

Hello, currently I'm working on a model based on Monte-Carlo-Simulations. I observed that a generated normal distributed times series using rnorm(100,mean=0,sd=1) is far away from being not autocorrelated. Is there any other gerenator implemented in R, which might solve my problem? -- View this message in context: