similar to: lme: problems with corARMA

Hi, I got a computational problem with lme (nlme library R 1.9.1) using corSymm(). Here is the data: [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.19639793 0.09127954 0.11733288 0.07598273 0.06545106 0.06211532 [2,] 0.22773467 0.10981912 0.16052847 0.38101187 0.18353474 0.24072918 [3,] 0.46743388 0.45733836 0.32191178 0.43356107 0.39159746 0.53984221 [4,]

Hi, I usually use lme from the nlme library. Now I have read an article about lmer in Rnews and lmer seemed to me more comfortable to use. Unfortunately, I didn't find out how to use covariance structures (e. g. corSymm(), corAR1()). Is there a way to use them similarly as in lme ? Is it implemented ? If somebody knows, please let me know. Thank you very much in advance, Stephan

I tried following commands: amp~time|subject/trial #this was the grouping structure of the data plot(dip,inner=~condition,layout=c(2,2)) after the plot command I obtained this error message: Error in if(!any(cond.max.level - cond.current.level <0)&&(row-1)* : missing value where TRUE/FALSE needed This error only occured in compination with "layout". It was no

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

Would anyone be able to provide insight for the following question, please? Setting: estimation of prediction intervals for age-period-cohort models using GAMs (rate ~ s(age,period)) Method: bootstrap (Davison and Hinkley, 1997) Issue: standardisation of the residuals for resampling requires an adjustment using the diagonals of the hat matrix. Is there a simple way to get the hat values out of a

it occupies the nerazzurri champions league final win over the psychological advantage, and for bayern speaking, lost the champions league blemish also have hope in February of knockouts compensated, however with seven months at the top of the highest in Europe now compared both teams are suffering a flourishing after the lonely time. After the end of the season, inter milan and bayern Munich are

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

Dear All: I am trying to fit a special case of a 2-banded Toeplitz correlation structure. A 2-banded Toeplitz has ones on the diagonal, a correlation, RHO1, on the first off-diagonal, and a correlation, RHO2, on the second off-diagonal, with zeros on all subsequent off-diagonals. After reading relevant sections in Mixed-Effects Models in S and S-PLUS (Pinheiro & Bates, 2000) and searching

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

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

I have a data analysis job for which lme may be used. Prof. Spencer Graves had helped me much on that. I'm really appreciated for that. Could anybody else in the list give me some hints from other perspectives? I hope I can learn as much as possible for this complicated real data. Thanks in advance. Hanhan To briefly describe my data: My data is health effect measurements (y) and personal

Hi I constructed a mixed-effects model from longitudinal repeated measurements of lab values in 22 patients seperated into two groups with the groups as fixed effect using lme. I thought about using the jackknife procedure, i. e., removing any one subject and calculating the fixed effect, to assess the stability of the fixed effect and thereby validate the model. I suppose this has been done in

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 R-sters, Just wondering what I might be doing wrong. I'm trying to fit a multiple linear regression model, and being ever mindful about the possibilities of autocorrelation in the errors (it's a time series), the errors appear to follow an AR1 process (ar(ts(glsfit$residuals)) selected order 1). So, when I go back and try to do the simultaneous regression and error fit with gls,

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 all, I have a performance problem in terms of computing time. I estimate mixed models on a fairly large number of subgroups (10000) using lme(.) within the by(.) function and it takes hours to do the calculation on a fast notebook under Windows. I suspect by(.) to be a poor implementation for doing individual analysis on subgroups. Is there an alternative and more efficient way for doing

Hi, Is it possible to create Rose Diagrams of wind data (speed & direction) with R?? Best regards, Lars Peters ----- Lars Peters University of Konstanz Limnological Institute D-78457 Konstanz Germany phone: +49 (0)7531 88-2930 fax: +49 (0)7531 88-3533 e-mail: Lars.Peters@Uni-Konstanz.de web: Lars Peters <http://www.uni-konstanz.de/sfb454/tp_eng/A1/doc/peters/peters.html>

Hello, I've got a big problem. I'm using R for geostatistical analyses, especially the field-package. I try to generate plots after the kriging process with help of image.plot(..., col=terrain.colors, ...). Everything works fine, but I want to reverse the color-palettes (heat.colors, topo.colors or gray()) to get darkest colors at highest data-values instead the other way round. Could

Dear all, I've got a big problem. I try to analyse my data using R with a mixed model ANOVA without useful results and success. My data are as follows: 3 factors (Treatment, Site, Subsite) with 'Subsite' as random factor and nested into 'Site'. I want to analyse the effects of the three main effects (factorial design to a specified degrees (2)) with the interactions between