similar to: split dataset randomly in prediction and validation set

Hi, I read somewhere that when carrying out geostatistical analysis in R you should not use latitude and longitude...can anyone expand on this a little for me, and what would be the best coordinate system to use? I have my data in a geographic coordinate system, WGS84, decimal degrees....is this the wrong format for such analyses? I have also converted my data in the UTM projection and so have

The NOAA National Centers for Coastal Ocean Science is hiring a Computational Ecologist, a statistical/computational ecologist with experience fitting advanced spatial models to marine wildlife survey data (e.g., seabirds and marine mammal transects, fisheries trawl surveys) in R and other statistical languages. This is a full-time, long-term stable contract position. We are looking for an

Hello everyone,  I'm working in a project to create an special analysis about visualization in some animals and its behaviour, through checking eye movement. The point is this is very important to get data from the image as a resume of what this animal sees. I think an RGB histogram like photoshop does, but I need to get the data from this histogram analysis and I was wondering about using R

Hello, I've got a big problem. I'm using R for geostatistical analyses, especially the field-package. I try to generate plots after the kriging process with help of image.plot(..., col=terrain.colors, ...). Everything works fine, but I want to reverse the color-palettes (heat.colors, topo.colors or gray()) to get darkest colors at highest data-values instead the other way round. Could

Dear all, Sorry for such basic question, but. when R "says": library(Rcitrus) Loading required package: geoR Loading required package: sp ------------------------------------------------------------- Analysis of geostatistical data For an Introduction to geoR go to http://www.est.ufpr.br/geoR geoR version 1.6-8 (built on 2006/06/29) is now loaded

Hi I have two geostatistical models from geoR. An ordinary kriging model with AIC=-148.6 and a universal kriging model with AIC=-156.7, there are 345 data points. The improvement shown by the AIC by adding a trend component to the model seems quite small given the number of data points, is there a test to see if the improvement to the model fit is significant? Thanks David

Hi, I am really new with R, so I don't know anything about it. I have written a script (attached) which tries to do really basic stuff (such as computing basic statistics and basic plots). When I try to plot a histogram and pairs, for example, I get the following message: > source("project.R") Loading required package: sp

Back on 18-June a colleague (Earl Glynn) asked: ================== I did a ?package.contents in version 1.9.0. The help file says this is deprecated, and says to "see also" Deprecated and Defunct, but what is the "new" function that replaces packge.contents? The function still seems to work, but it's just tagged as deprecated. efg ================== I have the

The majority of the functionality present in the gstat stand-alone program (http://www.gstat.org/) is now available as a package/library for the S language (R, S-Plus), again called gstat. The package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. Gstat was started 10 years ago and was released under the GPL in 1996; the

The majority of the functionality present in the gstat stand-alone program (http://www.gstat.org/) is now available as a package/library for the S language (R, S-Plus), again called gstat. The package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. Gstat was started 10 years ago and was released under the GPL in 1996; the

Hello all, I have searched and have not yet identified a solution so now I am sending this message. In short, I need to split my data into training, validation, and testing subsets that keep all observations from the same sites together ? preferably as part of a cross validation procedure. Now for the longer version. And I must confess that although my R skills are improving, they are not so

Full_Name: cajo ter Braak Version: 2.1.1 OS: Windows Submission from: (NULL) (137.224.10.105) # I would like to attach the matrix C in the Rdata file; it is 50x50 and comes from a geostatistical problem (spherical covariogram) > rm(list=ls(all=TRUE)) > load(file= "test.eigen.Rdata") > ls() [1] "C" "eW" > > sym.check = max(abs(C - t(C))) # should

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

Hi all, I use kriging to interpolate the precipitation from stations, but the map of this results show lots of stripes. (please see the attachment)I think there's something wrong with the setting of the dimension of this matrix, however, I have no idea how to know or test to see if this setting is correct or not.I've tried to switch the latitude and longitude, but still got the same

Hi All, I am a PhD student in forestry science and I am working with LiDAR data set (huge data set). I am a brand-new in R and geostatistic (SORRY, my background it?s in forestry) but I wish improve my skill for improve myself. I wish to develop a methodology to processing a large data-set of points (typical in LiDAR) but there is a problem with memory. I had created a subsample data-base but

<< repost because previous attempt was not plain text, sorry! >> Hi Folks, I have a pretty simple problem: after building a multivariate linear model, I need to report my 95% confidence interval for predictions based on future observations. I know that one option is to use predict(interval="prediction") but I'm curious about less parametric ways to get an estimate. I

Hi, My question might be a little general. I have a number of values to select for the complexity parameters in some classifier, e.g. the C and gamma in SVM with RBF kernel. The selection is based on which values give the smallest cross validation error. I wonder if the randomized splitting of the available dataset into folds is done only once for all those choices for the parameter values, or

Hello, I would like to know, whether for the support vector classification function ksvm() the response values stored in object at ymatrix are cross validated outputs/predictions: Example code from package kernlab, function ksvm: library(kernlab) ## train a support vector machine filter <- ksvm(type~.,data=spam,kernel="rbfdot",kpar=list(sigma=0.05),C=5,cross=3) filter filter at

Hi, I have a database of 2211 rows with 31 entries each and I manually split my data into 10 folds for cross validation. I build logistic regression model as: >model <- glm(qual ~ AgGr + FaHx + PrHx + PrSr + PaLp + SvD + IndExam + Rad +BrDn + BRDS + PrinFin+ SkRtr + NpRtr + SkThck +TrThkc + SkLes + AxAdnp + ArcDst + MaDen + CaDt + MaMG + MaMrp + MaSh +

Sir, I am working with multiclass discriminant analysis.(say response variable has 3classes).In R, using lda(), I get 2 sets of coefficients for the discriminant function.Now, I want to put a new x-vector(vector of independent variables) and want to check it corresponds to which class of y.Is there any formula for doing this? or how can I do this? Regards, Suman Dhara [[alternative HTML