similar to: Warning: Spatial weights matrix not row standardized

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 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

I am new to R and have a question about spatial econometrics. I have noticed that you can easily test for spatial autocorrelation with the spdep package, but was wondering if any code has been written to correct for spatial autocorrelation? Or if there is any literature on this? Thanks. -Jill *************************************************** Jill L. Caviglia-Harris, Ph.D. Assistant

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

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

I?m tryng to use the SPDEP package in my research in the field of population genetics. My data set has the following format: - x and y : coordinates, - Z: allelic frequency in each loci (in a total of 8 locis) - this variable can assume the values 0 ; 0.5 or 1. The objective is to verify if there is a possible spatial autocorrelation structure of the allelic frequency in a population of

Hi, I'm have some data on a grid (specifically high throughput assay plates) and am interested in evaluating measures of spatial autocorrelation to flag plates for corrections. I have been using moran.test and geary.test from the spdep package. My approach is as follows: ## plate is a matrix of data coords <- expand.grid(1:32, 1:48) x <- as.numeric(plate) moran.test(x,

Hello, Has anyone modified the errorsarlm in the R package spdep to allow for more than a single spatial autocorrelation coefficient (i.e. 'lambda')? Or, if not, any initial suggestions on how to make that modification? I have looked at the source code for the function and realize that any attempt to do it on my own would require much dedication, so would like to

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 all, I am attempting to create a weights object and perform a Moran I test as well. I have a very large spatial weights matrix (roughly 22,000x22,000) that was created in Excel and read into R, and I'm now trying to implement: library(spdep) SW=mat2listw(matrix) I am getting the following error: Error in if (any(x<0)) stop ("values in x cannot be negative"): missing value

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

hello which package do you recommend for spatial autocorrelation ? regards Ahmet Temiz ______________________________________ Inflex - installed on mailserver for domain @deprem.gov.tr Queries to: postmaster at deprem.gov.tr ______________________________________ The views and opinions expressed in this e-mail message are ...{{dropped}}

Hi: First my apologies for cross-posting. A few days back I posted my queries ar R-sig-geo but did not get any response. Hence this post. I am working on two parcel-level housing dataset to estimate the impact of various variables on home sale prices. I created the spatial weight metrics in ArcGIS 10 using sale year of four nearest houses to assign weights.  Next, I ran LM tests and then ran

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,

Dear all, I have a question regarding the spdep package: Assume that each person within my dataset is characterized by a continuous variable y. In order to test for spatial autocorrelation in y I calculated a Moran's I statistic. The problem is that y is also likely to be influenced by a series of other variables x that are unique to each person in my dataset. To test to which extent the

I want to do "moran's I test" in R language. I try to use "gearymoran" in Package "ade4","moran" in Package "spdep", and Moran.I in Package "ape". But I do not know how to do it because data format is different. My data: x y dbh 111.03 10.7 7 118.11 0.28 1.2 165.36 0.36 8.4

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

Hi list, I am trying to fit arima model for a grid of 360x161x338 points, where 360x161 is the spatial dimension and 338 is the number of time steps I have, which is seasonal. For this purpose I used the auto.arima function in forecast package. After fitting residuals at each grid in space, the auto correlations are still significant ( but < 0.2). This make me think that the data

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