similar to: geodata conversion

Sorry, this is probably quite an easy question, but I'm new to R and couldn't find the answer anywhere. I'm using geoR and geoRglm, but can't figure out how to get a border in my geodata object. Does this need to be defined when I'm importing my data, or afterwards, and how do I go about doing this? Thanks -- View this message in context:

Hello, I'm new to this list. Sorry if my question or parts of it already came up before. For my research in geostatistics, I am working with large sets of data in R (basically large matrices containing discrete x and y coordinates and a value for a certain parameter). These sets are obtained by kriging. The operation I'd like to perform is smoothen the output data set. I want to do it by

I've enconuntered this problem with the last cran version of geoR: > library(geoR) > day <- rep(1:2, each=5) > coords <- matrix(rep(runif(10),2), 10, 2) > data <- rnorm(10) > data[1] <- NA > as.geodata(cbind(coords, data, day), realisations=4) as.geodata: 1 points removed due to NA in the data Errore in as.geodata(cbind(coords, data, day), realisations = 4) :

Hola Javier, si, y es un factor de Y. Pero ya entendí lo que tenía mal, me falta convertir Nt a valores de geodata antes de hacer el plot, es decir: date<-as.geodata(Nt) plot.geodata(date) Con relación a la actualización de mi versión de R, no me he animado porque he tenido problemas con paquetes que no tienen las actualizaciones y no me corren bien. Gracias y saludos Beatriz 2013/8/6

Hi, I'm a new R user. My goal is to do a variogram using geoR. I started by trying to do the example in the geoR Illustrative Session using my own data. I am able to read in my Ascii data using: D <- matrix(scan("file.dat", n=530*3), 530,3, byrow=TRUE). Then I use: as.geodata(D, coords.col=1:2, data.col=3) to make the object D geodata. I check the descriptive statistics,

Hola a todos, estoy trabajando con dos archivos csv. En uno de ellos extraigo dos variables (que son coordenadas), del segundo archivo extraigo otra variable que transformo. Con estas tres variables construyo un data.frame (las tres variables tienen la misma longitud). Hasta ahí todo bien, solo que cuando quiero usar este data.frame para hacer un plot.geodata me marca el siguiente error Error en

Dear, we have a problem with geoR. We try to read an ASCII table (x,y,z) with 40000 lines. With read.geodata we get the error: Error in vector("double", length) : cannot allocate vector of length 799980000 We can read the file without any problem with read.table, but trying to convert it to the geodata class gets the same error. Any help/tips would be appreciated. Kris, Dave --

Hey there! I got a pseudo code and don't know how to apply it to R, maybe someone can help me: Input: A dataset X, kmax: maximum number of clusters, num_subsamples: number of subsamples. Output: S(i; k) - a distribution of similarities between partitions into k clusters of a reference clustering and clustering of subsamples; i = 1 to num_subsamples Requires: T = cluster(X): A hierarchical

I'm using R for Mac OSX version 1.14 (2129) and the geoR package version 1.6-5 (the current version in the R repository). I'm running R in OS 10.4.6 on a Mac G4 iBook (933MHz, 640 MB DDR SDRAM). I searched the R archive and did not find a posting on this issue. I want to use the variog and variog4 functions of geoR to characterize the pattern of spatial autocorrelation of tree

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform =

Hi, Im using geoR and I'm trying to do some predictions, based on an external trend. I'm having some problems specifying my model.control, specifically how do I define my model, and also the source of the covariate data at the prediction locations? I am assuming that the covariate data at the prediction locations should be imported to a geodata object along with the prediction location

Dear R users. I'm trying to fit a generalised linear spatial mode using the geoRglm package. To do so, I'm preparing my data (geodata) as follow: geoData9093 = as.geodata(data9093, coords.col= 17:18, data.col=15,* covar.col=16*) where covar.col is a factor variable (years in this case 90-91-92-93)). Then I run the model as follow: / model.5 = list(cov.pars=c(1,1),

Hi, I am trying to use piecewise linear regression to approximate a nonlinear function. Actually, I don't know how many linear functions I need, therefore, I want build an array of regression models to automate the approximation job. Could you please give me any clue? Attached is ongoing code: rawData = scan("c:/zyang/mass/data/A01/1.PRN", what=list(numeric(),numeric())); len =

Agradeceré me ayuden con la consulta: Cómo convertir un objeto data frame en una tabla csv que pueda a su vez ser convertida en un objeto geodata De antemano, gracias por la atención, -- Ricardo Bandin Llanos rbandin@udec.cl Estudiante - Magíster Cs. m. Pesquerías Universidad de Concepción, Región del Bio-Bio, Chile Celular: (0056-41) 97949957 [[alternative HTML version deleted]]

Dear all, Let say I have following data: RawData <- matrix(1:101, nr = 1); colnames(RawData) <- c("ASD", as.character(as.yearmon(seq(as.Date("2012-03-01"), length.out = 100, by = "1 month")))); rownames(RawData) <- "XYZ" CutOffDate <- as.Date("2012-09-01") NewDateSeries <- as.character(as.yearmon(seq(CutOffDate, to =

In an earlier post, a person wanted to divide each of the rows of rawdata by the row vector sens so he did below but didn't like it and asked if there was a better solution. rawdata <- data.frame(rbind(c(1,2,2), c(4,5,6))) sens <- c(2,4,6) temp <- t(rawdata)/sens temp <- t(temp) print(temp) Gabor sent three other solutions and I understood 2 of them but not the

I understand that in R, for loops are not used as often as other languages, and am trying to learn how to avoid them. I am wondering if there is a more efficient way to write a certain piece of code, which right now I can only envision as a for loop. I have a data file that basically looks like: 1,55 1,23 2,12 ... that defines a matrix. Each row of the data file corresponds to a row of the

Dear R, I import data from spss into a R data.frame. On this rawdata I do some data processing (selection of observations, normalization, recoding of variables etc..). The result is stored in a new data.frame, however, in this new data.frame the value labels are lost. Example of what I do in code: # read raw data from spss rawdata <- read.spss("./data/T50937.SAV",

Hi R -users, i've a table as describe below. I'm reading the numeric value presented in this table to populate a list. #table #============ #X    A    B    C #x1    2    3    4 #x2    5    7    10 #x4    2    3    5 #============ rawData <- read.table("raw_data.txt",header=T, sep="\t") myList=list() counter=0 for (i in c(1:length(rawData$X))) {     print (i)    

ComRades: I am geeting the error message Error in ldots[[which(MET)]] : attempt to select less than one element when I try to fit the geostatistical model with the likfit() function of geoR. I have tried with old data for which likfit() successfully maximised the likelihood in previous versions of geoR, and yet the current version fails. I have tried in Windows Vista and Windows XP (I haven't