search for: mat2listw

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 where TRUE/FALSE needed. What's going wrong here? My current matrix is all 0's and 1's, with no missing values and no negative elements. What am I missi...

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 where TRUE/FALSE needed. What's going wrong here? My current matrix is all 0's and 1's, with no missing values and no negative elements. What am I missi...

Hello everybody, I would like to use the SpDep-package (especially the Local Moran index analysis and the Getis-Ord statistics) in R for analysing my data. However, I don't have x-y coordinates, but my data is in a distance matrix format. Is it possible to use the SpDep package with predefined distances as well instead of letting the program determining the distance itself by the function

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,

Thanks Mike. My data has longitude and latitude coords and I used distAB {clim.pact} then mst {ape} to calculate my minimum spanning tree. The nodes are telecoms sites from all over Australia. My goal is to determine the minimum cost of linking them via cabling, and I'm starting by calculating the distance "as the crow flies", but will probably eventually need to calculate the

...0 0 1 0 1 0 0 [5,] 0 1 0 1 0 1 0 1 0 [6,] 0 0 1 0 1 0 0 0 1 [7,] 0 0 0 1 0 0 0 1 0 [8,] 0 0 0 0 1 0 1 0 1 [9,] 0 0 0 0 0 1 0 1 0 > z=mat2listw(W);z Characteristics of weights list object: Neighbour list object: Number of regions: 9 Number of nonzero links: 24 Percentage nonzero weights: 29.62963 Average number of links: 2.666667 Weights style: M Weights constants summary: n n1 n2 n3 nn S0 S1 S2 M 9 8 7 6 81 24 48 272

Hello everybody, I would like to determine kernel densities along a maze. I have distances from each point of the maze to its neighbours and I managed by means of the mat2listw-function in the Spdep package to assign neighbours to each point. Each point now has a value for a Z-variable and it is this variable that I would like to use to determine a density for each point with neighbours at 200m along each side. (I hope this makes some sense.) Does anyone have an idea...

useR's I need transform the matrix wdat [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0 1 0 1 1 0 [2,] 1 0 0 1 1 0 [3,] 0 0 0 0 1 1 [4,] 1 1 0 0 1 0 [5,] 1 1 1 1 0 0 [6,] 0 0 1 0 0 0 to gal object. How I do with spdep? Thanks in advance for the help. Bernardo. University of Concepci?.

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