similar to: analysing HTS assay plates for spatial effects

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

Hi, I have a question on testing spatial autocorrelation on raster data including NA-values. In particular, I like to calculate Moran?s I and Geary?s C indices by using inverse distance weighting matrices. Calculating Moran?s I with moran.test works fine, because the function contains the option "na.action=na.pass". Unfortunately, the function geary.test does not contain this

-----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

Consider the Assay data where block, sample within block and dilut within block is random. This model can be fitted with (where I define Assay2 to get an ordinary data frame rather than a grouped data object): Assay2 <- as.data.frame(Assay) fm2<-lme(logDens~sample*dilut, data=Assay2, random=list(Block = pdBlocked(list(pdIdent(~1), pdIdent(~sample-1),pdIdent(~dilut-1))) )) Now, block

Hello, I am interested in using Morans I for different time intervals to detect disease clusters. Ultimately I would like to use CUSUM - or similar monitoring statistic to monitor the results of Morans I - similar to the work by Rogerson (2005) Spatial Surveillance and Cummulative Sum Methods in Spatial and Syndromic Surveillance for Public Health. Thus far - thanks to the list I have

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 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

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

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<-

Dear R-users, I was wondering if someone could give me some advices on the following problem. I tried to apply moran’s test to a small dataset and couldn’t succeed, here is the error message:   mor <- moran.test(x, res2)   Avis dans moran.test(x, res2) :   Out-of-range p-value: reconsider test arguments   mor     Moran's I test under randomization data:  x  weights: res2 

Hello, I am looking for any packages, tutorials, documents,... about the use of R for the analysis of HTS data. Thanks for your help Fred [[alternative HTML version deleted]]

Dear all, I would like to implement a spatial quantile regression using instrumental variable estimation (according to Su and Yang (2007), Instrumental variable quantile estimation of spatial autoregressive models, SMU economics & statistis working paper series, 2007, 05-2007, p.35 ). I am applying the hedonic pricing method on land transactions in Luxembourg. My original data set contains

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

Have a look at http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example and http://adv-r.had.co.nz/Reproducibility.html On Wednesday, January 10, 2018, 11:51:22 AM EST, deva d <devazresearch at gmail.com> wrote: dear all, i need some help in structuring my data file for a hierarchical time series analysis. can someone help please ? i have a 600

Hello, Have a look at the plm package https://cran.r-project.org/web/packages/plm/index.html It has a convenient way to structure your data into panel according to some id. Best regards, Jeremie On Wed, Jan 10, 2018 at 5:41 PM, deva d <devazresearch at gmail.com> wrote: > dear all, > > i need some help in structuring my data file for a hierarchical time series > analysis.

Dear all, I am new here ,I attempted to use R to estimate the spatial Durbin (mixed) model,and mydata is a panel data form,and the matrix is generated by geoda software ,here is my Command and error,really hope your help ,thank you! #??gal library(spdep) w<- read.gal("E:/splm/zj.GAL",override.id=TRUE) ww<-nb2listw(w,zero.policy=TRUE) #???? library(foreign)

thanks jeff and jeremie, i am attaching 40 rows of the data, randomly picked from the large table. the vars are - entity (1-46, with some missing IDs not included due to missing data), group (1/2), sub group (1/2/3/4), year (2002-2016), y, x1 and x2 - large values included due to size of players - (may not be considered as outliers as they constitute the sample and are important countrywide

dear all, i need some help in structuring my data file for a hierarchical time series analysis. can someone help please ? i have a 600 row database in the nature of a panel data, with 3 time series values of interest. the data also has 4 classificatory variables comprising a code for each entity in the panel, a value for time (year), and classification of type of entity and a further sub-group