similar to: regions in Gabriel graph

Dear r-helpers, I have a query regarding use of contrasts in MANOVA. summary(manova(model)) gives me only result of test for overall difference. Would you be so kind and give me a hint how to get the same test statistics (e.g.Pillai's) and P values for the predefined contrasts? Best regards Ondrej Mikula -- Institute of Animal Physiology and Genetics Academy of Sciences of the Czech

Dear R-helpers, I have a number of point configurations representing skull shapes, but some of them contain superfluous points. I want to write a loop in which each configuration is plotted and I am asked to write the numbers of points that are superfluous. However, I don't know how to introduce this interactive element. Would you give me an advice? Best regards Ond?ej Mikula -- Ond?ej

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, is there a way how to add information to internal nodes (branching points) in dendrogram created via plot.agnes function (package cluster)? I wish to place colored circles on the nodes, but I don't know how to proceed... I'll be grateful for any suggestion Ond?ej -- Ond?ej Mikula Institute of Animal Physiology and Genetics Academy of Sciences of the Czech Republic Veveri 97,

Dear r-helpers, I have a graph created using the library 'spatgraphs'. library(spatstat) library(spatgraphs) xy <- rbind(c(28.39, -16.27), c(30.62, -20.13), c(32.25, -28.7), c(22.43, -27.22), c(27.5, -21.17), c(31.22, -24.52), c(17.93, -26.92), c(18.72, -17.95), c(24.15, -17.82), c(29.23, -22.85)) ow <- owin(xrange=range(xy[,1]), yrange=range(xy[,2])) pp <-

Dear r-helpers, I have a trouble with a package downloaded from sourceforge.net (namely the package 'kopls'). I installed it from the local zip file with the expected result > utils:::menuInstallLocal() package ?kopls? successfully unpacked and MD5 sums checked but when I tried to load it I obtained the following error message > require(kopls) Loading required package: kopls Failed

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 group, I'd like to do spatial analysis of my data using the spdep package. It appears that a file of class nb is necessary, but I do not find what that should look like and if there is a function that creates such file for me. How can I create a nb-object of my data points? Thanks, René Eschen. [[alternative HTML version deleted]]

Hello, I read a book about statistics in psychology. The authors use SPSS. They talk about post hoc tests after ANOVA finds significant effects: - Gabriel's procedure (for equal or slightly different sample sizes) - Hochberg's GT2 (for different sample sizes) - Games-Howell procedure (for populations with unequal variances) I could not find them in R. Do they not exist in R

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

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