similar to: Bug in the function Variogram (package nlme) ?

Dear Users, >From previous analysis (semi-variograms using package gstat), I found spatial autocorrelation in my dataset. The best fitted model to this spatial correlation structure is the Gaussian model (Spherical, Exponential, Linear tested and comparison done by Sum of Square errors). So I used corGaus function to define this spatial autocorrelation in my lme model using the option

Hello, I am having a problem specifying the correlation structure in lme which leads to the error: Error in getCovariate.corSpatial(object, data = data) : Cannot have zero distances in "corSpatial". I have specified a grouping variable which is the only fix I could find by searching this error on R-help. ISee the below example. When my samples (tran) - which are transects

Dear all, I'm trying to fit a variogram model using variofit function in geoR package. There is an option to fix the nugget but is there anyway to force the variogram sill to equal some defined value? I'm working with standardized longitudinal data so the process variance is 1 and I'd like to force the sill of the variogram to reach this value (and prevent it from going

I am trying to use the nlme package in R following the book "Mixed-Effects Models in S and S-Plus". I have the latest versions of R (1.6.1) and nlme (3.1-33) for Windows (95 or later). I had problems with the instruction Variogram using a gls class object (Section 5.4, pg 261-262). Even when I use the scripts included in the package, I get an error message and not the expected

When I try to run the example from Variogram with an lme object, I get an error (although summary works): R : Copyright 2005, The R Foundation for Statistical Computing Version 2.2.1 (2005-12-20 r36812) ISBN 3-900051-07-0 ... > fm1 <- lme(weight ~ Time * Diet, BodyWeight, ~ Time | Rat) Error: couldn't find function "lme" > Variogram(fm1, form = ~ Time | Rat, nint =

I noticed that ?corClasses in package nlme does not list corSpatial among the standard classes. This might either be intentional because corSpatial is not "standard" , or it might be simply an oversight that needs correcting. ------------------------------------------ Robert W. Baer, Ph.D. Professor of Physiology Kirksville College of Osteopathic Medicine A. T. Still University of

Hi, I have a spatial data set with many observations (~50,000) and would like to keep as much data as possible. There is spatial dependence, so I am attempting a mixed model in R with a spherical variogram defining the correlation as a function of distance between points. I have tried nlme, lme, glmmML, and glmmPQL. In all case the matrix needed (seems to be (N^2)/2 - N) is too large for my

Dear listers, I am trying to model the distribution of fox density over years in the Doubs department. Measurements have been taken on 470 plots in March each year and georeferenced. Average density is supposed to be different each year. In a first approach, I would like to use a general model of this type, taking spatial correlation into account:

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

Dear R Help, I am having difficulty using Variogram within GLS to examine spatial structure of nested data. My data frame consists of ecological measurements of a forest, in which three landscape positions ("landposi") are compared. Each landscape position is replicated five times ("replicat"), and the environment is measured ("canopy", "litdepth", etc.)

Dear R users, I'm estimating a mixed effects model in which the spatial correlation is controlled for by the "corGaus" structure. I'm wondering if there is a document or paper that explains how the spatial correlation structure (such as "corExp" or "corGaus") works. Let me use the example and data posted on UCLA's R FAQ webpage to explain my problems.

Dear Members, I am trying to fit a variogram model using fit.variogram function from the gstat package. The figure showing my experimental variogram can be seen here: https://i.stack.imgur.com/UZXw4.png My code line for this operation is: > c2.vgm.fit<-fit.variogram(c2.vgm.exp,vgm(nugget=0, psill=400,model="Exp",range =40000),fit.method = 7) The system throws following error

Dear All, Is there anybody has the experience in using variogram(gstat) ? Please kindly give me some hints about the results. I used variogram() to build a semivariogram plot as: tr.var=variogram(Incr~1,loc=~X+Y,data=TRI2TU,width=5) then fir the variogram to get the parameters as: v.fit = fit.variogram(tr.var,vgm(0.5,"Exp",300,1)) v.fit model psill range 1 Nug 1.484879

Hi, Well after trying to fix the assign commands in version 3.1.7 of your nlme package I still cannot get the plot functions to work. Basically I have no other packages installed (apart from those provided with R) so nlme is the first. my R version info is; platform sparc-sun-solaris2.7 arch sparc os solaris2.7 system sparc, solaris2.7 status

Dear R Helpers I'm investigation the geostatistics tools in R and have found the package 'gstat', which looks to be useful four two dimensional data. However, I usually deal with three dimensional information. I would like to compute a 3D variogram map using the variogram tool but cannot seem to get it to recognise the third (z) dimension The data file reads ok and the spatial

I have spatial data on a sphere (the Earth) for which I would like to run an gls model assuming that the errors are autcorrelated, i.e. including a corSpatial correlation in the model specification. In this case the distance metric should be calculated on the sphere, therefore metric = "euclidean" in (for example) corSpher would be incorrect. I would be grateful for help on how to

Hi List,   I’ m analysing the selectivity of resting site use by forest carnivores through mixed modelling techniques and I wonder which will be the best r package to deal with several aspects simultaneously: -          binomial variable response; -          possible spatial and/or temporal correlation; I have tried nlme (lme function) and lme4 (lmer function) packages, however I realize that the

Hello, I am attempting to get the standard deviation in multiple distance bins in my spatial data. It appears as though the 'variog' command in the geoR package will do the trick, as one of the outputs from 'variog' is 'variog$sd', which, according to the manual, is the "standard deviation of the values in each bin". However, when I run this command, the

I wonder what is the default value for the argument 'cutoff' when not specified in the variogram.formula function of gstat. Computing variogram envelops within gstat, I am comparing the results obtained with variog in geoR and variogram in gstat, and it took me a while before understanding that the cutoff default value is not the maximum distance. Can Edzer tell us about it? All the

Dear R-help, The following nlme example, from help(Variogram.gls), does not work on my system. > data(BodyWeight) > fm1 <- gls(weight ~ Time * Diet, BodyWeight) > Variogram(fm1, form = ~ Time | Rat)[1:10,] Error in FUN(X[[1]], ...) : unused argument(s) (method ...) It looks like I might not have loaded a necessary package. I did load nls and lattice (the latter not actually being