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...bout labels and coordinates) with number inputs like : 110,248,245,151,175,165,163,52,213,315,164,276,273,273,175,220,284,216,213,278,245,157,278,248 My problem appear when I want to create such plots with inputs composed by strings like: CORPUS,CORPUS,CORPUS,CORPUS,CORPUS,CORPUS,CORPUS,CORPUS,OVARY,OVARY,OVARY,OVARY,PERITONEUM,PERITONEUM,PERITONEUM,PERITONEUM,PERITONEUM,PERITONEUM,PERITONEUM,PERITONEUM,PERITONEUM,UTERUS,UTERUS,UTERUS for creating plots of the distributions of the different 'words'. (e.g. bar named corpus that tell me how many time it is repeated, and so on). I hope...

...et, but I'm not sure where to begin looking. I can run through the Pinheiro & Bates example on p.240 in a simplified form, so I know that everything with my installation (R v2.4.1; nlme v3.1-79; Windows XP) is working fine. However if I construct a groupedData object similar in form to the Ovary example, I will receive the same error, regardless of the model I specify. I can plot the groupedData object and get a figure very similar to Fig. 5.10 in Pinheiro & Bates. I have provided summarized output with a very simple lme() model, and the exact message. Any ideas on why the error is...

...instead of envir. So I changed these and still recieved and error telling me that envir was being misused. From what I could see the envir were being used in default mode so I simply removed them. Now when I run the code I get the following; library(nlme) Loading required package: nls > data(Ovary) > fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary, + correlation = corAR1(form = ~ 1 | Mare)) > plot(fm1, resid(., type = "p") ~ fitted(.) | Mare, abline = 0) [[1]] coplot $show.given [1] FALSE (and a very blank graph screen). Also if I run the follo...

...ocate the second of the objects. The following code, borrowed from the help page of anova.gls, exemplifies: --------------- start example code --------------- library(nlme) ## stolen from example(anova.gls) # AR(1) errors within each Mare fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary, correlation = corAR1(form = ~ 1 | Mare)) anova(fm1) # variance changes with a power of the absolute fitted values? fm2 <- update(fm1, weights = varPower()) anova(fm1, fm2) ## now define a little function dummy <- function(obj) { anova(obj[[1]], obj[[2]]) } dummy(list(fm1,...

...functions with sampling effort (measured in time units) as a distance measure. As a result I got several segfault errors (which I don't know what they exactly mean =/). I was wondering if it was an effect of the model or the data I used, but I was able to reproduce the error messages using the Ovary data set and the example in the Pinheiro & Bates book: >library(nlme) >data(ovary) >fm10var.lme <- lme(follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time),data=Ovary, random=pdDiag(~sin(2*pi*Time))) >fm50var.lme <- update(fm10var.lme,c...

Hi there, I try to compare the mixed model package "lme" by Splus and R. I used the dataset "Ovary" and the following code assuming AR(1) model for the error term: lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), data=Ovary, random = pdDiag(~sin(2*pi*Time) ) , correlation=corAR1() ) But I got different results! And then I used a simpler model: lme(follicles ~ sin(2*pi*Time) + cos(2*pi*T...

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 to here with all parameter estimates being identical to that in the book. fm2<-update(fm1,correlation=corAR1()) #### The parameters of fm2 are different to that in the...

...The end of nlme-Ex.Rout is: > rm(list = ls(all = TRUE)); .Random.seed <- c(0,rep(7654,3)) > ###--- >>> `anova.gls' <<<----- Compare Likelihoods of Fitted Objects > > ## alias help(anova.gls) > > ##___ Examples ___: > > library(nlme) > data(Ovary) > # AR(1) errors within each Mare > fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary, + correlation = corAR1(form = ~ 1 | Mare)) Warning message: NA/Inf replaced by maximum positive value > anova(fm1) Denom. DF: 305 numDF F-value p-value (...

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 : Coeffici...

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 mixed-effects model fit by REML Data: Ovary Log-restricted-likelihood: -771.9471 Fixed: follicles ~ sin(2 * pi * Time) + cos(2 * pi * Time) (I...

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

...on. I refitted the model, and the same error message appeared. I then refitted the model with option corr = corARMA(p=1), then no problem; but for p = 2, or q = 1 or 2, then the error occurred. When I listed the same fitted nlme objects under R 2.1.1 with nlme 3.1-65, then no problem. I fitted the Ovary data (Pinheiro and Bates 2000, p.397) using the script provided in nlme package fm3Ovar.nlme <- update(fm1Ovar.nlme, correlation = corARMA(p=0, q=2)), and tried to list the result. The same error occurred. I tried it out on several of PCs (WINXP SP-2, R 2.2.1, nlme 3.1-68) and the same situatio...

...test.2 2 7 135.1308 145.3909 -60.56539 1 vs 2 0 1 HOWEVER, for the example in chapter 5.3 of the book in which an autoregressive structure is used for the within group errors, I get the following error: > test <- lme(follicles~sin(2*pi*Time)+cos(2*pi*Time),data=Ovary,random=pdDiag(~sin(2* pi*Time))) > test.ar1 <- lme(follicles~sin(2*pi*Time)+cos(2*pi*Time),data=Ovary,random=pdDiag(~sin(2* pi*Time)),correlation=corAR1()) > intervals(test.ar1) Error in intervals.lme(test.ar1) : Cannot get confidence intervals on var-cov components: Non-positive definite...

Greetings. I'm teaching linear regression this semester and that means I write more functions for my regression support package "rockchalk". I'm at a point now were some fresh eyes would help, so if you are a student in a course on regression, please consider looking over my package overview document here: http://pj.freefaculty.org/R/rockchalk.pdf That tells how you can grab

I'm looking at Pinheiro & Bates "Mixed-Effects Models in S and S-PLUS" at the moment. Several datasets are used, one of which is called "PBIB" (a partially balanced incomplete block design). All the other datasets can be found somewhere or other in R. However, I cannot locate PBIB, and it does not seem to be mentioned in the latest edition of the R Full Reference

I'd like to know why I cannot get a plot and the QQnorm in the same sheet. The commands are simple but: library(nlme) glmod1 <- gls(upfmla,correlation=corAR1(),method="ML") summary(glmod1) par(mfrow = c(2,1)) plot(glmod1, main="GLS Residuals vs. GLS Fitted") qqnorm(glmod1) No matter what (I tried different permutations of the plotting commands) the second drawing

Hi useRs - I was wondering if anyone out there can tell me where to find R-code to do mixes of tables and graphics. I am thinking of something similar to this: http://yost.com/information-design/powerpoint-corrupts/ or like the excel routines people are demonstrating: http://infosthetics.com/archives/2006/08/excel_in_cell_graphing.html My aim is to provide small graphics to illustrate

Having spent the last few weeks trying to decipher R, I feel I may finally be getting somewhere, but i'M still in need of some advice and all my tutors seem to be on holiday! Basically a bit of background, I have data collected on a population of Lizards which includes age,sex, and body condition. I collected data myself this year and I have data previously collected from 1999, 2002 and

Hola:   Tengo un modelo de regresión lineal en el cual las componentes son cosenoidales, y lo construyo del siguiente modo:     modelo = "y ~ I(t) + I(t^2) + I(t^3) + x1[, 1] + x2[, 1]" x1[, 1] = cos(2 * pi * t / periods[1]) x2[, 1] = sin(2 * pi * t / periods[1]) for (i in 2:nComp) {   x1[, i] = cos(2 * pi * t / periods[i])   x2[, i] = sin(2 * pi * t / periods[i])   modelo =

  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