similar to: Repeating functions

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

Philipp Benner reported a Debian bug report against r-cran-rpart aka rpart. In short, the issue has to do with how rpart evaluates a formula and supporting arguments, in particular 'weights'. A simple contrived example is ----------------------------------------------------------------------------- library(rpart) ## using data from help(rpart), set up simple example myformula <-

Hello, guys. Q1. now I have load all the docid and its document data into a dictionary for faster loading data instead of calling Xapian::MSetIterator i; i.get_document().get_data(); but I was happened to discover that the dictionaries got by such two method were different: both methods use DB1, DB2 method 1: Xapian::Database db = Xapian::Database(the path of DB1); Xapian::Database db2 =

Hello, I can't figure out why using and not using weights in mlogit yields identical results. My motivation is for the case when an "observation" or "individual" represents a number of individuals. For example, library(mlogit) library(AER) data("TravelMode", package = "AER") TM <- mlogit.data(TravelMode, choice = "choice", shape =

Hello! I wrote a code that works, but it looks ugly to me - it's full of loops. I am sure there is a much more elegant and shorter way to do it. Thanks a lot for any hints! Dimitri # I have a data frame: x<-data.frame(group=c("group1","group2","group1","group2"), myweight=c(0.4,0.6,0.4,0.6),

Hi, I am a new student to R and I was hoping someone could help me with the error message I keep getting when I try to use the nb2listw() function. I have 45 sites and I want to get an autocorrelation value for sites within 1000m of each other. coords<-cbind(EAST,NORTH) coords<-as.matrix(coords) nb1000<-dnearneigh(coords,0,1000,longlat=TRUE) then when I try to run the

Hi, While searching for a project which matches my interest andskill level, I found this project named Matcher Optimization. This project is really challenging and excting from my view point and I would like to be a part of this project. Optimization techniques metioned in the reference links provided will take some time for me to have a good understanding about them. But I am trying to get my

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

Hello I'm very new to R (which is excellent), so apologies if this has already been raised. In the spdep package, I'm trying to undertake an autoregressive mixed model using the lagsarlm function. This is working fine, but there does not appear to be a method of including an explanatory variable without it automatically being included as a lagged term. I'm after something along the

Dear List, I have some problem with my simulation code. Here is output from R: > sim.sp <- function(data,CM,n,N) + { + C <- matrix(rep(NA,N),ncol=1) + for(i in 1:N) + { + j <- n + xx <- which(colSums(CM[j,])==1) + V <- names(xx) + V <- paste(V, collapse="+") + V <- paste("SBA~", V) + rd <- round(nrow(data)*(2/3)) + d <-

Can someone tell me what an object of class lm returned by lm means? I assumed it mean the regression model - but I'm not sure how to enter this in. I have tried y~a+b but this is not working. I have also tried saving the regression results and entering these, but again this is incorrect. This language is from the following: lm.LMtests(model, listw, zero.policy=FALSE,

Dear List, I have got error message when I run the R-code. Can anyone has a suggestion? v.code <- df.bm7[,c(10:31)]; v.code[1:3,] names(v.code) CM = v.code # variable binomial code sim.sp <- function(data,CM,n,N) { C <- matrix(rep(NA,N),ncol=1) for(i in 1:N) { j <- n xx <- which(colSums(CM[j,])==1) V <- names(xx) V <- paste(V,

-----Original Message----- From: r-help-request at stat.math.ethz.ch [mailto:r-help-request at stat.math.ethz.ch] Sent: Tuesday, July 01, 2003 3:09 AM To: r-help at stat.math.ethz.ch Subject: R-help Digest, Vol 5, Issue 1 At 18:48 30/06/2003, Martin Wegmann wrote: >hello, > >I want to do a test for spatial correlation. >I tried it with geary.test() but I don't understand the

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

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'm currently trying to calculate local Getis-Ord Gi* statistics for a 169x315 cell matrix of temperature values, below is the code I currently have (diffc is the data vector I am removing NaN values from, and I am moving said values to diffD; -999 represents NaN values; id contains ID values for cells I want to use in the calculation, which I already know to contain 25064 values):

Hello, I'm new to R and to Spatial Analysis and got a problem trying to create a Spatial Weights Matrix. *I us the following code to create the Neighbourslist:* >library(maptools) >library(spdep) >library(rgdal) >location_County<- readShapePoly("....") >proj4string(location_County)<- CRS("+proj=longlat ellps=WGS84") >location_nbq<-

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 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

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