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...function gls. The anova procedure fails to locate 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) { anov...

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*Time), data=Ovary, random = pdDiag(~sin(2*pi*Time)) ) This time I got the same outpu...

...Max. :8 Max. :99.13 > lme(CHEAL~rel.year,data=cheal.grp)->cheal.lme Error in y[revOrder] - Fitted : non-conformable arrays #### Everything works fine using the Pinheiro & Bates example #### on page 240 (in a simplified form) > summary(Ovary) Mare Time follicles 8 : 31 Min. :-0.1667 Min. : 1.00 4 : 29 1st Qu.: 0.1667 1st Qu.: 8.00 5 : 29 Median : 0.5000 Median :12.00 1 : 29 Mean : 0.5000 Mean :12.04 6 : 29 3rd Qu.: 0.8333 3rd Qu.:15.00 10 : 29 Max. : 1.1667 Max. :25.00...

...anged 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 following (plot.variogram example)...

...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,correlation=corARMA(p=1,q=1)) >fm10var.nlme <- nlme(follicles ~ A + B * sin(2 * pi * w * Time) + C * co...

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 : Coefficient matrix not inverti...

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: -771.947...

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 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 book and plot(ACF(fm...

...1 6 133.1308 141.9252 -60.56539 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 v...

Often, when exploring a dataset, I'd like to plot several very different Y variables against the same X variable, in panels stacked one over the other. Is there an easy way to do this? I'd like to achieve an elegant look similar to the look achieved by lattice in conditioned plots--for instance no space between panels. But unlike in straightforward conditioned plot, each panel may be on a

....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 (Intercept) 1 354.7375 <.0001 sin(2 * pi * Time)...

...<- data01[[2]] y <- data01[[3]] data02 <- ppp(x, y, xrange=c(0,512), yrange=c(0,512)) unitname(data02) <- c("pixel", "pixels") plot(data02) #etc etc #I can also import my text images and convert them to a tessellation using the following: a <- read.table("FOLLICLES.txt") win <- owin(c(0,512), c(0,512)) unitname(win) <- c("pixel", "pixels") b <- as.matrix(a, xrange=c(0,512), yrange=c(0,512)) unitname(b) <- c("pixel", "pixels") c <- im(b, xcol=seq(ncol(b)), yrow=seq(nrow(b)), unitname="pixels&quot...

...AT issue, so VPN can resolve? Note: anyone knows if h323 works better in the IP trunk? Regards Bilal ---------------------------------- yeah i found openvpn helpful in NAT cases. -Vivek On 11/6/07, Baji Panchumarti <baji.panchumarti at gmail.com> wrote: > > after a copious loss of follicles :-), I finally got outbound working. > > Basically the channel statement in the call file needs to have the > number to be called. For eg., in test.call format the statement > as follows : > > Channel: SIP/3012345678@<your-sip-provider> > > And there is no need f...

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

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