similar to: How to fit the log Gaussian Cox process model

Dear Michael, my package has setOldClass("inla") and the NAMESPACE contains exportClasses(inla) and importFrom(INLA, inla.posterior.sample) the old version of INLA uses the S3 class "inla" but does not export it. the new version of INLA has setOldClass("inla") and the NAMESPACE contains exportClasses(inla) installing my package in combination with an older INLA

Dear all, Today I got this error message (R 3.6.1) when installing my package: Error: package or namespace load failed for ?inlatools? in namespaceExport(ns, exports): undefined exports: .__C__inla My package was using setOldClass("inla") and exported the "inla" class via the NAMESPACE. It imports functions from the INLA package. Older versions of the INLA package did not

I obtain this message: Error: C stack usage is too close to the limit Marc Marí-Dell'Olmo CIBER Epidemiología y Salud Pública Servei de Sistemes d'Informació Sanitària (SeSIS) Agència de Salut Pública de Barcelona Pl. Lesseps 1. 08023 Barcelona Tel. 93 2027775 | Fax. 93 3686943 www.aspb.cat El 15 de marzo de 2012 13:35, Marc Marí Dell'Olmo <mmari@aspb.cat> escribió: >

I'm searching a method to estimate the hyper-parameters in arima models. I'm reading about r-inla package, but in the examples section only talk about the AR part of the arima, but i need help about the MA part too. I'm beginner in Bayesian methods, I'm reading the documentation about dlm package and kalman filters, but the computacional cost of inla i think is better, but only

We are pleased to announce the following book: Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA Authors: Zuur, Ieno, Saveliev Book website: www.highstat.com Paperback or EBook can be order (exclusively) from www.highstat.com TOC: http://highstat.com/Books/BGS/SpatialTemp/Zuuretal2017_TOCOnline.pdf Summary: We explain how to apply linear regression models,

Just to clarify, your package is exporting a class that is not defined? Or is it exporting a class that is defined by a dependency and then masked by setOldClass()? A simple reproducible example would help. On Fri, Sep 6, 2019 at 7:48 AM Thierry Onkelinx via R-devel <r-devel at r-project.org> wrote: > > Dear all, > > Today I got this error message (R 3.6.1) when installing my

Hello, I have fitted a Log-Gaussian Cox Process on my data but when I try to use "simulate.kppm" of the spatstat package I get this error: Error in rLGCP(model = model, mu = mu, param = param, ..., win = win) : The spatial domain of the pixel image ?mu? does not cover the simulation window ?win? I've used covariates as im, changed the npixel value of the spatstat options to

We would like to announce the following statistics course: Course: Introduction to Linear Mixed Effects Models and GLMM with R Where:? Lisbon, Portugal When:?? 19-23 February 2018 Course website: http://highstat.com/index.php/courses Course flyer: http://highstat.com/Courses/Flyers/2018/Flyer2018_02LisbonV2.pdf Kind regards, Alain Zuur -- Dr. Alain F. Zuur Highland Statistics Ltd. 9 St

A long formula which is converted using as.character, looses its last part: ``diagonal = 1e-12)'' Shorter formula is ok though. Best, H??vard ************ Browse[2]> formula.str y ~ -1 + b1 + b2 + b3 + b4 + b5 + b6 + b7 + b8 + b9 + b10 + b11 + b12 + b13 + b14 + b15 + b16 + b17 + b18 + b19 + b20 + b21 + b22 + b23 + b24 + b25 + b26 + b27 + b28 + b29 + b30 + b31 + b32 +

Hi all, I'm running into some computer issues when trying to run a binomial model for spatially correlated data using glmmPQL and was wondering if anyone could help me out. My whole dataset consists of about 300,000 points for which I have a suite of environmental variables (I'm trying to come up with a habitat model for a species of seal, using real (presence) and simulated dives

Hi, I'm trying to build a mixed-effects model in which I'd like to include either a distance matrix or a phylogenetic tree as a random effect. The troubles I've had are that: 1. Function lmer() in package lme4 only accepts a data frame column as a random factor and not a distance matrix. 2. Function MCMCglmm() in package MCMCglmm only accepts a rooted and ultrametric phylogenetic

Hi, I have a situation where I have a set of pairs of X & Y variables for each of which I have a (fairly) well-defined PDF. The PDF(x_i) 's and PDF(y_i)'s are unfortunately often rather non-Gaussian although most of the time not multi--modal. For these data (estimates of gas content in galaxies), I need to quantify a linear functional relationship and I am trying to do this as

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component gaussian mixture -- where the components have a large overlap, and I am trying to use the "mclust" package to solve this problem. I need

Dear R Helpers, I have searched for any R package or code for simulating multivariate fractional Brownian motion (mFBM) or multivariate fractional Gaussian noise (mFGN) when a covariance matrix are given. Unfortunately, I could not find such a package or code. Can you suggest any solution for multivariate FBM and FGN simulation? Thank you for your help. Best Regards, Ryan ----- Wonsang You

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Hi All, I am using em() function to estimate a poisson-gaussian process from a univariate one dimension time series, but not sure how to do. In the help manual, it specify that in "pro" of the argument "parameter", if the model includes a Poisson term for noise, there should be one more mixing proportion than the number of Gaussian components. But in the example, the parameter

Hi All, I am trying to use A Gaussian quadrature over the interval (-infty,infty) with weighting function W(x)=exp(-(x-mu)^2/sigma) to estimate an integral. Is there a way to do it in R? Is there a function already implemented which uses such weighting function. I have been searching in the statmode package and I found the function "gauss.quad(100, kind="hermite")" which uses

Hello, I'm Emanuela, I'm implemented a survival analysis and I'm trying to use a frailty model with inverse gaussian distribution, but I'm not able to find the right code, because it seems to be only a gamma and a gaussian distribution. Is there also the inverse gaussian distribution? Thanks a lot Emanuela Rossi

Dear all, I am trying to fit a double gaussian to some data using the mixdist package: --- begin code --- ? library(mixdist) ? time <- seq(673,723) ??counts <- c(3,12,8,12,18,24,39,48,64,88,101,132,198,253,331,419,563,781,1134,1423,1842,2505,374,6099,9343,13009, ? ? ? ? ? ? ? ? ? ?15097,13712,9969,6785,4742,3626,3794,4737,5494,5656,4806,3474,2165,1290,799,431,213,137,66,57,41,35,27,27,27) ?

Hello, I am using the parameterized EM- Algorithm in clustering context. I use generated datasets, containing 5000 points in 20 dimensions with 5 clusters. The following poses a problem for me: emEst<-me(modelName="EEV",data=data[,1:numDimensions],z=unmap(x)) emEst<-me(modelName="VEV",data=data[,1:numDimensions],z=unmap(x)) coordProj(main="Model