similar to: analyzing binomial data with spatially correlated errors

Dear all, I am experiencing problems with glmmmPQL. I am trying to analyze binomial data with some spatial autocorrelation. Here is my code and some of the outputs > colnames(d.glmm) [1] "BV" "Longitude" "Latitude" "nb_pc_02" "nb_expr_02" [6] "pc_02" "nb_pc_07" "nb_expr_07"

Dear all, I am starting to use the lme package (and plan to teach a course based on it next semester...). To understand what lme is doing precisely, I used balanced datasets described in Pinheiro and Bates and tried to compare the lme outputs to that of aov. Here is what I obtained: > data(Machines) > summary(aov(score~Machine+Error(Worker/Machine),data=Machines)) Error: Worker

Dear all, I am trying to simulate data sets from a model fitted with glmmPQL, in order to compute the distribution of a summary statistics. My data are binomial and I have a correlation term in my model. My model is structured in the following way m <- glmmPQL( fixed = cbind(sucess,failure) ~ x1 + x2 + ... , random = ~ 1 | bidon, correlation = corGaus(form=~

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

Hello All, I have a macro and want to jump to another macro if a conditition is true or false. Asterisk is jumping to the next macro, but then the {ARG1} variable is not working anymore. part of config: [macro-default] exten => s,1,DBGet(do-not-disturb=DND/${ARG1}) exten => s,2,GotoIf($["${do-not-disturb}" = "YES"]?macro-do_not_disturb,s,1) ... [macro-do_not_disturb]

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:

Hi all, I wrote a piece of code that generates simulated variables. after variable generation I use them in several analyzes. However, when I use a for to repeat the procedure 1000 times I get an erro message in one of the "for" steps, precisely at this time: gls.temp<- gls(y2 ~ x2,correlation=corExp(form=~coord2[,1]+coord2[,2])) # coord 2 are spatial coordinates and the error

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML

I'd like to compare two models which were fitted using gls, however I'm having trouble interpreting the results of gls. If any of you could offer me some advice, I'd greatly appreciate it. Short explanation of models: These two models have the same fixed-effects structure (two independent, linear effects), and differ only in that the second model includes a corExp structure for

Hi, I just registered. I have a problem related to wine: lmms can run some vst plugins with VeSTige, so I tried to get ez drummer working. It didn't work, so I downloaded Toontrack Solo, that is a program needed to run ez drummer as a standalone, and not as a plugin. It still wouldn't work, neither as a standalone. I made a few searches and I found out that it needed mfc42.dll so I

A couple weeks ago I posted a message on this topic to r-help, the response was that this seemed like odd behavior, and that I ought to post it to one of the developer lists. I posted to r-sig-mixed-models, but didn't get any response. So, with good intentions, I decided to try posting once more, but to this more general list. The goal is (1) FYI, to make you aware of this issue, in case it

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

[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

Hi, I started using R about 3 months ago so please excuse my ignorance. I have two datasets. The first consists of 247 fixed position mosquito traps that were serviced weekly for 23 weeks. The second is dengue incidence data indexed to home address for the same time period. I would like to see if there is a space time relationship between mosquito catch rate and dengue incidence. So far I have

Hi. I'm trying 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)

Hi, I am trying to extract the coefficients matrix from a gls summary. Contrary to the lm function, the command fit$coefficients returns only the estimates of the model, not the whole matrix including the std errors, the t and the p values. example: ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <-

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

Hi: The gls function I used in my code is the following fm<-gls(y~x,correlation=corARMA(p=2) ) My question is how to extact the AR(2) parameters from "fm". The object "fm" is the following. How can I extract the correlation parameters Phi1 and Phi2 from "fm"? These two parametrs is not in the "coef" componenet of "fm". Thanks a

Hello folks, I have some data where spatial autocorrelation seems to be a serious problem, and I'm unclear on how to deal with it in R. I've tried to do my homework - read through 'The R Book,' use the online help in R, search the internet, etc. - and I still have some unanswered questions. I'd greatly appreciate any help you could offer. The super-super short explanation is

Dear List, I am realtively inexperienced so i apologise in advance and ask for understanding in the simplicity of my question: I have data on the amount of grass per km in a cell ( of which i have lots) "grass" and for each cell i have x/y coordinates - required due to spatial autocorrelation Cells can be classfied in a hierarchical nature into AREAS and STATES i.e Cell 1, Cell 2,