similar to: How to create a "nb" object?

hi,friends, we all know that moran's I index and Geary'C index can be used to test spatial autocorrelation in both the area data and point data, but i only can find something on how to calculate on the data of area, and can't find the methods to perform it on the point data, could anybody give me some information, thanks in advance! -- Kind Regards, Zhi Jie,Zhang ,PHD Department of

Hola Freddy. Yo parto de un shape en que tengo los típicos archivos .shp, .sbx, .sbn, .shx y .dbf. En la tabla tengo el área de los poligonos en m2, el perímetro, el estado y el código ISO del estado dispuesto por el SGM. ¿Me puedes orientar un poco más? Gracias, Sebastían. El 14 de agosto de 2013 16:19, Freddy Omar López Quintero < freddy.vate01@gmail.com> escribió: > Hola

Dear All, We aim to remove the spatial structure of our data using Moran Eigen Vectors and spdep package . Our data has 3694 samples and 13 variables. The computer stop working after almost 4 days of processing (we found it emitting a sharp sound and with all colors on the screen. No wories, it was restared without problem!). And we are left with nothing: no result file was produced since the

Hi all, I'm using R 2.0.1. for Windows to analyze the influence of following factors on response Y: A (four levels) B (three levels) C (two levels) D (29 levels) with E (four replicates) The dataset looks like this: A B C D E Y 0 1 1 1 1 491.9 0 1 1 1 2 618.7 0 1 1 1 3 448.2 0 1 1 1 4 632.9 250 1 1 1 1 92.4 250 1 1 1 2 117 250 1 1 1 3 35.5 250 1 1 1 4 102.7 500 1 1 1 1 47 500 1 1 1 2 57.4

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

Hello group, For my PhD I'm working on a spatial sampling grid. I do have two data sets which I'd like to compare using cross-correlograms or cross-variograms. Is this an option in one of the R-packages? I've been searching the R-help archive and the available package-documentations, but I can't find how to do this. Thanks in advance, Ren?.

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

All, I'm trying to analyze the results of a reciprocal transplant experiment using lme(). While I get the error-term right in aov(), in lme() it appears impossible to get as expected. I would be greatful for any help. My experiment aimed to identify whether two fixed factors (habitat type and soil type) affect the development of plants. I took soil from six random sites each of two types

I downloaded a US states+territories shapefile from http://www.weather.gov/geodata/catalog/national/html/us-state.htm and unzipped it into my working directory. Then in R (version 12.2.0 for ms-win32): library(rgdal) # Version: 0.6-28 library(spdep) # Version: 0.5-24 (loads sp: Version: 0.9-72) # read in the data states<-readOGR(".","s_01de10") summary(states) # now

Hi group, I'm trying to do a Tukey test to compare the means of a factor ("treatment") with three levels in an lme model that also contains the factors "site" and "time": model = response ~ treatment * (site + time) When I enter this model in csimtest, it takes all but the main factor "treatment" as covariables, not as factors (see below). Is it

Why are the results not reliable? ________________________________ From: ESCHEN Rene [mailto:rene.eschen@unifr.ch] Sent: Wednesday, August 23, 2006 3:48 AM To: Spencer Graves; r-help@stat.math.ethz.ch Cc: Doran, Harold Subject: RE: [R] Random structure of nested design in lme The output of the suggested lmer model looks very similar to the output of aov, also when I ran the model

Dear R users, I've written a loop to generate Moran's test (spdep package) on serval subsamples of a large dataset. See below a short example. My loop is working fine, however I would like to be able to store the test results as lines of a matrix, that I would latter be able to export as a dataset. My problem is that I'm not sure how I could do this using R. Any help will be much

Estimados usuarios de R: Estoy probando la econometría espacial reproduciendo el paper Introduction to Spatial Econometrics: An application to the study of fertility in Argentina using R. de Herrera Gómez, Marcos; Cid , Juan Carlos and Paz , Jorge Augusto. La diferencia es que voy a usar datos de Uruguay del Censo 2011. Ellos utilizan GeoDaSpace para calcular la matriz de contigüidad (tambien

Group, I'm currently trying to find out how the function correlog in the ncf package may be useful to me for calculating cross-correlograms. The function's output includes P-values for all distance classes, but it seems that only positive values can become significant. Is this true and correct? If so, why? Thanks in advance. Ren? Eschen. _______________________________________ Ren?

Hello, I am new to R and running R version 3.4.3 (2017-11-30), x86_64-apple-darwin15.6.0 (64-bit), macOS High Sierra 10.13.2. I am running the gam package to model disease incidence (negative binomial distribution) as a function of two covariates, and wish to incorporate spatial correlation among areal neighbors, n = 50 polygons, identified by "id". For data observed over discrete

Dear R users, I'm trying to construct a distance matrix based on a nb object created by spdep where sites must have a larger influence in one direction then in the other. Here is an example to better illustrate what I need: Let say I have the following Gabriel connection network library(spdep) library(ade4) data(orbatid) nbgab<-graph2nb(gabrielneigh(as.matrix(oribatid$xy)))

Dear list, following the vignette of the package 'spacemmakeR' I have built a gabriel graph of ten randomly placed points: library(spdep) set.seed(3) xyir<-matrix(runif(20),10,2) nbgab<-graph2nb(gabrielneigh(xyir), sym=TRUE) plot(nbgab,xyir,points=FALSE) points(xyir,pch=20,cex=2,col=c(2,4,4,4,2,2,4,2,4,4)) title(main="Gabriel Graph") Now, I need to determine for any

Hi Are there any more efficient ways of calculating the neighbourhood object for large datasets? I am trying to compute Moran I statistics for a very large data set (over 14,000 points). I have been using moran.test from the spdep package and everything works fine for a small data set (200 points). However, applying the same script to the whole dataset is taking days to compute (it so far has

I get this message, when opening the winecfg, rn@MX:~$ winecfg fixme:wave:ALSA_AddCaptureDevice Add support for DSCapture fixme:wave:ALSA_AddCaptureDevice Add support for DSCapture fixme:mixer:ALSA_MixerInit No master control found, disabling mixer /bin/sh: /usr/bin/esd: not found fixme:jack:JACK_drvLoad error loading the jack library libjack.so, please install this library to use jack --------

hello, I want to do a test for spatial correlation. I tried it with geary.test() but I don't understand the required input. x= a numeric vector the same length as the neighbours list in listw (my sampled data, I assume) listw= a listw object created for example by nb2listw (well when I check nb2listw() I get to "neighbours - an object of class nb" - but I couldn't figure