search for: corspati

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 sampled several times - have differen...

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 Med...

...ave been attempting this repeated measure analysis where correlation among observations (within each subject) is spatial. My command is: >fdat.lme1<-lme(Lint~Rep+Treatment,data=fdat,random=~1|fldPos,correlation=corLin(form=~lat+lon)) It is generating follwoing error: Error in getCovariate.corSpatial(object, data = data) : Cannot have zero distances in "corSpatial" first I thought that decimal values of latitude/longitude are resulting in extremely small values of distances, causing R to ignore it. So, I scaled it up and made the coordinates integer values. But I am still getting...

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 write a new distance metric for the corSpatial function. I...

...m of Square errors). So I used corGaus function to define this spatial autocorrelation in my lme model using the option "correlation". The Variogram function (package nlme) used on a lme object calculates the semi-variogram for the within-group residuals and add the semi-variogram of the corSpatial element (corGaus in my case) included in my model ... so far no problem. I was surprised, however, to see on the plot of the semi-variogram issued from the Variogram function, (see figure at http://imm.io/3OLe) the low range value (~1600 meters) used in the corGaus structure included in the lme...

Hi, I am trying to place my own functions in the nlme environment: The following statement works: environment(coef.corSPT) <- environment(getS3method("coef","corSpatial")) but this one returns an error: environment(get("coef<-.corSPT")) <- environment(getS3method("coef<-","corSpatial")) Error in environment(get("coef<-.corSPT")) <- environment(getS3method("coef<-", : target of assignme...

...first approach, I would like to use a general model of this type, taking spatial correlation into account: gls(IKAfox~an,correlation=corExp(2071,form=~x+y,nugget=1.22),data=renliev) but I get > gls(IKAfox~an,correlation=corExp(2071,form=~x+y,nugget=1.22),data=renliev) Error in getCovariate.corSpatial(object, data = data) : Cannot have zero distances in "corSpatial" I understand that the 470 geographical coordinates are repeated three times (measurement are taken each of the three years at the same place) which obviously cannot be handled there. Does anybody know a way to...

...to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS) with form = ~ x+y (and a nugget if appropriate). However, my data are binomial, so I need a different approach. Using various packages I could define a mixed model (eg using glmmPQL() in MASS) with similar correlation structure...

...x+y), family="poisson") ## proof that glmmPQL really is looking ## outside of the data frame for its ## correlation variables ... x = y = rep(0,100) glmmPQL(z~1,random=~1|f,data=d, correlation=corExp(form=~x+y), family="poisson") ## Error in getCovariate.corSpatial(object, data = data) : ## Cannot have zero distances in "corSpatial" > sessionInfo() R version 2.9.2 (2009-08-24) i486-pc-linux-gnu locale: LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.UTF-8;LC_MONETARY=C;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8...

...eatly appreciated. Kind regards, Geert > gamm3<-gamm(count~offset(offsetter)+s(lon,lat), random=list(code_tripnr=~1),family="poisson", correlation = corExp(1, form=~X + Y),nite rPQL=200) Maximum number of PQL iterations: 200 iteration 1 iteration 2 Error in recalc.corSpatial(object[[i]], conLin) : NA/NaN/Inf in foreign function call (arg 1) > gamm3<-gamm(count~offset(offsetter)+s(lon,lat,k=c(1,1)),random=list(code_tripnr=~1),family="poisson", niterPQL=200) Maximum number of PQL iterations: 200 iteration 1 iteration 2 Error in lme.formula(...

...tructure (two independent, linear effects), and differ only in that the second model includes a corExp structure for spatial autocorrelation. (more detailed explanation of the models at end). Specific questions: 1. The second model estimates two additional parameters in the process of fitting the corSpatial object - the range and nugget of the spatial autocorrelation. Based on this, I would expect the second model to have two fewer residual degrees of freedom. However, the summary function reports that both models have the same number of residual degrees of freedom. Why is this? (Interestingly, the...

....io/3OLe) Note that the semi-variance exceed 1 !! It can probably explain why the Gaussian correlation structure fitted in my "lme" model is strange (curve is probably fitted to the data above 1). See my previous message a few minutes ago untitled "Variogram (nlme) of a lme object - corSpatial element question." I don't know if it is a bug, but Implications could be important for model fitted with a spatial correlation structure. Arnaud [[alternative HTML version deleted]]

Dear all, I am trying to define my own corStruct which is different from the classical one available in nlme. The structure of this correlation is given below. I am wondering to know how to continue with this structure by using specific functions (corMatrix, getCovariate, Initialize,...) in order to get a structure like corAR1, corSymm which will be working for my data. Thanks in advance.

Hi, I have spatially autocorrelated data (with a binary response variable and continuous predictor variables). I believe I need to do an autologistic model, does anyone know a method for doing this in R? Many thanks C Bell

Hi All, I've been having a little trouble with creating a loop that will run a a series of t.tests for inspection, Below is the code i've tried, and some checks i've looked at. I've used the get(paste()) idea as i was told previously that the use of the eval should try and be avoided. I've run a single syntax to check that my systax is correct and works without any problems