similar to: lme:correlationstructure AR1 and random factor

Dear posters, I have a question concerning binary data analysis. I have presence absence data of 5 sampling sessions within 3 years, of 12 fields. Each field had 12 traps. I would like to analyse the data with a Generalized Estimating Equations (GEE) Model in R. For the abundance data I used a gls with the function: correlation = corARMA(form = ~ session|trapfield, p = 1, q = 0) But now I want

Dear R-help, I don't understand why Gls gives me an error when trying to fit a model with AR(2) errors, while gls (from nlme) does not. For example: library(nlme) library(rms) set.seed(1) d <- data.frame(x = rnorm(50), y = rnorm(50)) gls(y ~ x, data=d, correlation = corARMA(p=2)) #This works Gls(y ~ x, data=d, correlation = corARMA(p=2)) # Gives error # Error in

Dear R-helpers, I have a time series analysis problem in R: I want to analyse the output of my simulation model which is proportional cover of shrubs in a savanna plot for each of 500 successive years. I have run the model (which includes stochasticity, especially in the initial conditions) 17 times generating 17 time series of shrub cover. I am interested in a possible periodicity of shrub

Hi, I'm a new member here in the list. I am a graduate from University of Georgia. Recently in doing analysis using lme on a dataset, I found several questions: 1. How to express the equation when the correlation structure is very complicated. For exmaple, if the fixed is y(t)=0.03x1(t)+1.5x2(t)(I omitted "hat" and others). And the model with corARMA(p=2,q=3) is proper. What will be

  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 am currently trying to get a "lme" analyses running to correct for the non-independence of residuals (using e.g. corAR1, corARMA) for a larger data set (>10000 obs) for an independent (lgeodisE) and dependent variable (gendis). Previous attempts using SAS failed. In addition we were told by SAS that our data set was too large to be handled by this procedure anyway (!!). SAS script

Full_Name: Benjamin Tyner Version: 2.6.0 RC 2007-10-01 r43043 OS: WinXP Submission from: (NULL) (171.161.224.10) platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status RC major

HELLO ALL, I AM GETTING AN ERROR MESSAGE WHEN TRYING TO RUN A GAMM MODEL LIKE THE ONE BELOW. I AM USING R VERSION 2.14.1 (2011-12-22) AND MGCV 1.7-12. M1 <-gamm(DepVar ~ Treatment + s(Year, by =Treatment), random=list(Block=~1), na.action=na.omit, data = mydata, correlation = corARMA(form =~ Year|Treatment, p = 1, q = 0)) THIS IS THE ERROR MESSAGE Error in `*tmp*`[[k]] : attempt to

Dear Madams/Sirs, Hello. I am using the gls function to specify an arma correlation during estimation in my model. The parameter values which I am sending the corARMA function are from a previous fit using arima. I have had some success with the method, however in other cases I get the following error from gls: "All parameters must be less than 1 in absolute value". None of

Probably a very simple query: When I try to plot a curve from a fitted polynomial, it comes out rather jagged, not smooth like fitted curves in other stats software. Is there a way of getting a smooth curve in R? What I'm doing at the moment (for the sake of example) is: > x <- c(1,2,3,4,5,6,7,8,9,10) > y <- c(10,9,8,7,6,6.5,7,8,9,10) > b <- data.frame(cbind(x,y)) >

Dear All: I updated my R program as well as associated packages yesterday. Currently my R version is 2.2.1 running under WINXP SP-2. When I tried to list (summary) an nlme object that I developed before, I got the following error message: [ Error in .C("ARMA_constCoef", as.integer(attr(object, "p")), as.integer(attr(object, : C entry point "ARMA_constCoef"

Trying following example from Pinheiro and Bates in order to fit an ARMA(1,1) model: library(nlme) fm1Ovary.lme<-lme(follicles~sin(2*pi*Time)+cos(*pi*Time),data=Ovary,random=p dDiag(~sin(2*pi*Time))) fm5Ovary.lme<-update(fm1Ovary.lme,corr=corARMA(p=1,q=1)) I get follwing error message: Error in "coef<-.corARMA"(`*tmp*`, value = c(62.3428455941166, 62.3428517930051 :

It is a trivial problem, but in the book I couln`t figure out how to put different colours at different regression lines plot(bif,abund,type="n", xlab= "number_bifurcations", ylab="abundances") sbif<-split(bif,stage) sabund<-split(abund,stage) points(sabund[[2]],sbif[[2]],pch=16, col="red") for(i in 1:2) abline(lm(sabund[[i]]~sbif[[i]])) Thanks in

Dear all I am tryiing to fit the following lme with an ARMA correlation structure: test <- lme(fixed=fev1f~year, random=~1|id2, data=pheno2, correlation=corARMA(value=0.2, form=~year|id2), na.action=na.omit) But I get the following error message: Error in getGroupsFormula.default(correlation, asList = TRUE) : "Form" argument must be a formula I have used this same form

Hi, I am trying out a generalized least squares method of forecasting that corrects for autocorrelation. I downloaded daily stock data from Yahoo Finance, and am trying to predict Close (n=7903). I have learned to use date functions to extract indicator variables for Monday - Friday (and Friday is missing in the model to prevent it from becoming full rank). When I run the following code...

Hi, I am using 2 times series and I want to carry out a regression of Seri1 by Serie2 using structured (autocorrelated) errors. (Equivalent to the autoreg function in SAS) I found the function gls (package nlme) and I made: gls_mens<-gls(mening_s_des~dataATB, correlation = corAR1()) My problem is that I don’t want a AR(1) structure but ARMA(n,p) but the execution fails :

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 there fellow R-users, I am trying to follow an example of modelling a serial correlation structure in the textbook "Mixed Effects Model in S and Splus". However, I am getting some very odd results. Here is what I am trying to run: library(nlme) data(Ovary) fm1<-lme(follicles~sin(2*pi*Time)+cos(2*pi*Time),data=Ovary,random=pdDiag(~s in(2*pi*Time))) ### The example is fine up

I need to specify a model similar to this lme.formula(fixed = sqrt(lbPerAc) ~ y + season + y:season, data = cy, random = ~y | observer/set, correlation = corARMA(q = 6)) except that observer and set are actually crossed instead of nested. observer and set are factors y and lbPerAc are numeric If you know how to do it or have suggestions for reading I will be grateful. eal ps I have

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