similar to: nlme, MASS and geoRglm for spatial autocorrelation?

Hi, Currently object.size() is not very useful on environments as it always returns 56 bytes, no matter how big the environment is: env1 <- new.env() object.size(env1) # 56 bytes env2 <- new.env(hash=TRUE, size=75000000L) object.size(env2) # 56 bytes env3 <- list2env(list(a=runif(25000000), L=LETTERS)) object.size(env3) # 56 bytes This makes it pretty useless on

Hi Gabe, On 09/29/2015 02:51 PM, Gabriel Becker wrote: > Herve, > > The problem then would be that for A a refClass whose fields take up N > bytes (in the sense that you mean), if we do > > B <- A > > A and B would look like the BOTH take up N bytes, for a total of 2N, > whereas AFAIK R would only be using ~ N + 2*56 bytes, right? Yes, but that's still a *much*

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 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

Hello, I''m using the following expression to format a list: $valid_environments = [''env1'', ''env2'', ''env3''] $env_message = $valid_environments.reduce |$message, $env| { "${message}, ${env}" } It works at run-time (Puppet 3.2.4 standalone with "--parser=future"). However in Eclipse (v4.3.1),

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 R-people: Working in geoRglm, it shows me, according to AIC criterion, that the non-spacial model describes the process in a better way. It's the first time that I'm facing up to. These are my results: OP2003Seppos.AICnonsp-OP2003Seppos.AICsp #[1] -4 (OP2003Seppos.lf0.p<-exp(OP2003Seppos.lf0$beta)/(1+exp(OP2003Seppos.lf0$beta))) #P non spatial #[1] 0.9717596

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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),

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

Hello, I would like to fit generalized linear mixed models but I did not find the package allowing such procedure. R help under nlme package gives me "glmmPQL(MASS)" but this file does not appear in contributed packages. Thanks in advance for your answer. Alexandre MILLON -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Greetings all The help file for GAMM in mgcv indicates that the log likelihood for a GAMM reported using summary(my.gamm$lme) (as an example) is not correct. However, in a past R-help post (included below), there is some indication that the likelihood ratio test in anova.lme(mygamm$lme, mygamm1$lme) is valid. How can I tell if anova.lme results are meaningful (are AIC, BIC, and logLik

Dear R-listers, I would like to carry out a very basic descriptive analysis of homicides rates in Italy, taking into account both the spatial dimension (103 provinces) and the temporal dimension (10 years), but no covariates. In practice, what I would like to do is to describe spatio-temporal variation of homicide rates, identifying those combinations of province-year where the homicide rate

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, all, When running glmmPQL(), I keep getting errors like Error: cannot allocate vector of size 61965 Kb Execution halted This is R-1.7.1. The data set consists of about 11,000 binary responses from 16 subjects. The model is fixed = SonResp ~ (ordered (Stop) + ordered (Son)) * StopResp, random = ~ 1 + (ordered (Stop) + ordered (Son)) * StopResp | Subj family = binomial (link =

Dear all, I am experiencing problems using the glmmPQL function in the MASS package (Venables & Ripley 2002) to model binomial data with spatial autocorrelation. My question - is the presence of birds affected by various hydrological parameters? Presence/absence data were collected from 83 sites and coupled against hydrological data from the same site. The bird survey sampling effort

[I think I've seen this reported before but can't locate it any more. I believe this oddity (glitch? feature?) is behind a query that Jean-Baptiste Ferdy asked a year ago <http://finzi.psych.upenn.edu/Rhelp08/2008-October/176449.html>] It appears that glmmPQL looks in the global workspace, not within the data frame specified by the "data" argument, for the variables

Greetings, May you please suggest a package or function to use for fitting a GLMM (generalized linear mixed model) with spatially correlated random effects? Thank you, Elijah DePalma [[alternative HTML version deleted]]

Dear R users, I want to explain binomial data by a serie of fixed effects. My problem is that my binomial data are spatially correlated. Naively, I thought I could found something similar to gls to analyze such data. After some reading, I decided that lmer is probably to tool I need. The model I want to fit would look like lmer ( cbind(n.success,n.failure) ~ (x1 + x2 + ... + xn)^2 ,

hi, am trying to understand speex's algo. have a few questions. 1) autocorrelation: in the function, _spx_autocorr (for floating point version), there is a line ac[0] += 10; correct me if i am wrong, i suppose the addition of 10 is used to condition the autocorrelation matrix. wonder how the value of 10 is arrived at? 2) levinson durbin (LD) algo in the function _spx_lpc, i referred