similar to: Problem with nlme version 3.1-68

  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

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 :

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

Hi, All: Is there a way to get random effects for ARMA parameters? Consider the following example from the 'corARMA' help page: fm1Ovar.lme <- lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), data = Ovary, random = pdDiag(~sin(2*pi*Time))) fm5Ovar.lme <- update(fm1Ovar.lme, corr = corARMA(p = 1, q = 1)) fm5Ovar.lme Linear

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

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

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 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

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

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 out there, I am trying to fit a species accumulation curve (increase in number of species known vs. sampling effort) for multiple regions and several bootstrap samples. The bootstrap samples represent different arrangements of the actual sample sequence. I fitted a series of nlme-models and everything seems OK, but since the observations are correlated I tried to include some correlation

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 :

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 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

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)) >

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

Dear helpers, I tried these models to run in the package nlme, but allways got the same error message... I have a correlation in 5 sessions within a field (n=12) with ten traps in one field. res2a <- lme(response~x+y+z+treatment),correlation = corARMA(form = ~ session|trapfield, p = 1, q = 0), random=~1|field, na.action=na.omit, data=plates, method="ML") res2a <-

R-helpers, n <- 100 arcoefs <- c(0.8) macoefs <- c(-0.6) p <- length(arcoefs) q <- length(macoefs) require(nlme) tmp <- corARMA(value=c(arcoefs,macoefs), form=~1, p=p, q=q) Sigma <- corMatrix(tmp, covariate = 1:n) # results in segfault Have I used these commands in an improper way? Thanks Ben

I am working with panel data. I am using the plm package to do this. I would like to do be able to adjust for autocorrelation, as one does with glm models and correlation structures (eg corr=corARMA(q=4)) . In particular, I want to employ MA(4) error structure. Is there a way of doing this with the plm package? (Note: I do not really want to use the pggls function for various