similar to: geoR : Passing arguments to "optim" when using "likfit"

Mzabalazo Ngwenya wrote: > Hi everyone ! > > I'm am trying to fit a kriging model to a set of data. When I just run > the "likfit" command I can obtain the results. However when I try to > pass additional arguements to the optimization function "optim" I get > errors. That is I want to obtain the hessian matrix so matrix > (hessian=TRUE). >

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 I'm using geoR package to perform linear spatial interpolation(OK). The function likfit() fails to compute REML. The error meassage is : Error in solve.default(v$varcov, xmat); How I can find out that likfit() is failed to process and retrieving the error message ? Thank you so much for your help.

We want to compute a pseudo R-squared for a model whose parameter estimation was based on maximum likelihood (function likfit, package geoR). I tried to compute the R2 proposed by Maddala (1983) which compare the maximized likelihood for the model without any predictor and the maximized likelihood for the model with all predictors. I got a really low value (0,01%). Did I miss something? Are

I have been modelling spatial data using function likfit in package geoR. Now that I have the spatial regression models, I would like to calculate the amount of variance explained by both the trend and the spatial parts of the model. Any advice on how to do this would be much appreciated. Cheers, Ailsa

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch [SMTP:r-help-bounces at stat.math.ethz.ch] On Behalf Of ernesto > Sent: Friday, January 13, 2006 9:25 AM > To: Mailing List R > Subject: [R] help with gepRglm::likfit.glsm > > Hi, > > I'm exploring likfit.glsm and I need some help. I have to say that I'm > not an MCMC expert ... >

Greetings comRades: Has anyone worked out a procedure to calculate the estimated covariance between the beta and lambda estimates in the likelihood-based geostatistical model implemented in package geoR? If so, please help me out by letting me know. Do I have reasons to expect that this covariance term would be big or perhaps more happily, that it could be discarded without much effect?

I am having difficulty getting the trend.spatial function in geoR to work properly. After creating a trend.spatial object with a covariate, I try to add the command into my likfit() function as follows: trend1.trend.spatial <- trend.spatial("1st", trend1.geodata) trend1.spatial.EC0.1.reml <- likfit(spatial.geodata, trend1.trend.spatial, ini.cov.pars = spatial.EC0.1.eyefit,

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),

Dear Aaron, I am really a tool user and not a tool maker (actually an ecologist doing some biostatistics)... so, I take the liberty of sending a copy of this e-mail to the r-help list where capable computer persons and true statisticians may provide more relevant information and also to Paulo Ribeiro and Peter Diggle, the authors of geoR.. I really feel that your huge matrix cannot be

Hi, I'm writing some R code that requires a massive amount of looping and would ideally like to write it so that it avoid the use of "for" loop ... however I'm having some trouble. Very briefly, the basic idea is to implement a binary partitioning algorithm to determine the optimal cutpoint based on deviance measures obtained from likelihood estiamtes. This is in the

Hello, I used glsm.mcmc and likfit.glsm to create model. Now I want to predict at different locations, but I can't get glsm.krige to work. I keep getting the error that trend.d and trend.l must have similar specifications. I have tried numerous ways to include the covariates in the glsm.krige model, and I keep getting the same error message. The bolded part is the part that doesn't work.

Hi, I want to automatically fit variograms to a large number of different sample data sets, and call the funtion "likfit" (in package geoR) from within a for-loop. "likfit" does again call "optim". After ssuccessfully fitting variograms to some of the data sets, the procedure crashes and I get the error message: Error in optim(par = ini, fn = negloglik.GRF,

Dear R users: I want to model mosquito count data based on landcover attributes and meteorological variables using a Poisson GLSM in the geoRglm package. I have monthly mosquito counts over more than 20 years with repeated observations from individual trap sites over time. I have used as.geodata() to successfully read my dataset into the geodata format utilized by geoR and geoRglm,

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

Hi I am struggling to fit a non-linear trend using the likfit function in geoR. Specifically I want a sigmoidal function, something like SSfpl in the nls package to fit the trend. But it seems trend.spatial in geoR only works with lm or glm type models. Any ideas how I can specify the model to calculate the kriging parameters using REML, including the parameters of a sigmoidal trend function

Hi, R-folks: I have been tryin many combination of parameter to make Matern variogram to work, but I can't find the available one. I'm near to be crazy. I tiped: A?o2003Selg.lf<-likfit(A?o2003Selg,cov.model="matern",ini.cov.pars=c(1.5,14),kappa=2.5,fix.kappa=FALSE,nugget=0.08,lambda=0.008,fix.lambda=FALSE,hessian=TRUE) the hessian shows: $hessian [,1]

Hi, I have been able to generate association rules for Market Basket Analysis using the following codes: **************************************************************************** ******************************************* library("arules") rules <- read.csv("write1.csv",na.strings=c(".", "NA", "", "?"),header=TRUE)

Hi All, I'm trying to use the geoRglm package to run a poisson spatial glm on a dataset with several covariates. When I run without covariates I have no problems. control1.data.geo <- mcmc.control(S.scale=0.2, thin = 1) model1.data.geo <- list(cov.pars = c(1,1), beta=c(1), family="poisson") test1.model1 <- glsm.mcmc(data.geo, model=model1.data.geo,

Hello R-Users, its again me with a question. I´m using R 2.5.0 on Mac Power Book running Mac OS X 10.4.10 I try to calculate distances betweeen two data tables looking like this V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 1 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0