similar to: Question spdep package - Moran's I

Dear all, I would like to calculate a Moran's I statistic using the moran function in the spdep package. The problem I'm having deals with how to create the listw object. My data stems from the area of social network analysis. I have list of poeple and for each pair of them I have a measure of their relationship strength. So my dataset looks like: Jim; Bob; 0.5 This measure of

Hi, I am trying to calculate Moran's I test for the residuals for a regression equation, but I have trouble converting my coordinates into nb format. I have used the dnearneigh() funtion now with an arbitrarily high upper distance to make it include all plots. However, when I do the lm.morantest() I get a Moran's I value which is the same as the expected value and a P-value of 1. I

Dear , This is Elaine. I am computing moran's I using moran.test for a generalized linear model (multiregression). The following contents are the results, and I cannot find the observed Moran's I mentioned as estimate in the manual. Please kindly help indicate if there is observed Moran's I did not notice or other method for calculation. Thanks Elaine data: residuals(modg1)

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My question is in regard to the spdep and GeoDa packages. Do you know how to extract a neighborhood/adjacency matrix from R? I need a matrix that has all the neighborhoods on the X and Y axis, and then a r=1 when the neighborhoods touch, and a r=0 when neighborhoods don't touch. I think the Moran's I functions use a matrix like this when they estimate Moran's I. I can estimate

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

Hello- I am using the vegan package PCMN() because I need to use option of row weights (I am using CCA following the selection of the significant/positive PCNM variables). In the library PCNM (PCNM function), the Moran's I is calculated for you as part of the function. I see that I need to calculate Moran's I separately in order to retain the eigenfunctions with positive spatial

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

Hi, I am looking for some code for Moran's I. Has anyone previously done this? I have been unable to find it in the search engines. James

Hello all, I'm a new user of R and I have used the spdep package to calculate Moran's I estimates. I used the dnearneigh() function and moran.test() function and obtained what I wanted, but now I want to plot my results with the p-values. I regrouped my results for the Moran's I like >m=c(m1$e,m2$e...) #m1 contain the results of the moran.test for lag1

Dear list: I've tried professor Roger Bivand's spdep package for a while, and found it is quite useful. However, when considering the significance test of the local moran's index under the assumption of both normality and randomization, I just can't get a clue from the package's calculating results. I also read professor Luc Anselin's 1995 LISA paper (geographical

i would like to perform moran's analysis with r... thanks duccio

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

Dear all, I have a large dataset (N=100,000 with 89 variables per subject). This dataset is stored in a 100.000 x 89 matrix where each row describes one individual and each column one variable. What is the easiest way of selecting a subset of let's say 1.000 individuals out of that whole matrix? Thanks, Michael Michael Haenlein Associate Professor of Marketing ESCP-EAP European School of

Dear all, is there a convenient way to determine the effect size for a regression coefficient in a multiple regression model? I have a model of the form lm(y ~ A*B*C*D) and would like to determine Cohen's f2 (http://en.wikipedia.org/wiki/Effect_size) for each predictor without having to do it manually. Thanks, Michael Michael Haenlein Associate Professor of Marketing ESCP Europe Paris,

Dear all, I am trying to simulate a series of networks that have characteristics similar to real life social networks. Specifically I am interested in networks that have (a) a reasonable degree of clustering (as measured by the transitivity function in igraph) and (b) a reasonable degree of degree polarization (as measured by the average degree of the top 10% nodes with highest degree divided by

Dear all, I have several character strings with a high number of different levels. unique(x) gives me values in the range of 100-200. This creates problems as I would like to use them as predictors in a coxph model. I therefore would like to convert each of these strings to a new string (x_new). x_new should be equal to x for the top n categories (i.e. the top n levels with the highest

Dear all, I would like to estimate a system of regression equations of the following form: y1 = a1 + b1 x1 + b2x2 + e1 y2 = a2 + c1 y1 + c2 x2 + c3 x3 + e2 Specifically the dependent variable in Equation 1 appears as an independent variable in Equation 2. Additionally some independent variables that appear in Equation 1 are also included in Equation 2. I assume that I cannot estimate these two

Dear all, I have written a relatively brief R-Code to run a series of simulations. Currently the code runs for a very long time (up to several days, depending on the conditions) and I expect this to be the case because it might not be very efficiently written. I am, for example, relying on several for(...) loops which could probably be done much faster using a different way of programming. I am

Dear all, I have a discrete distribution showing how age is distributed across a population using a certain set of bands: Age <- matrix(c(74045062, 71978405, 122718362, 40489415), ncol=1, dimnames=list(c("<18", "18-34", "35-64", "65+"),c())) Age_dist <- Age/sum(Age) For example I know that 23.94% of all people are between 0-18 years, 23.28%