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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) { an...

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 out...

...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 *...

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 inver...

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: -771.9...

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(...

...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...

...conditioned plot, each panel may be on a different scale. Example. ? Plot Estrogen, Creatinine, and their ratio; all by the same predictor variable (say, Day). Or: In a longitudinal study of hormones in reproductive-age women, plot progesterone, estradiol, testosterone, luteinizing hormone, follicle-stimulating hormone, and thyroid-stimulating hormone all on one page, parallel. Note that several of these variables are measured in different units. ? One panel for each outcome variable, arranged one above the other. ? Minimal vertical space between the panels. To make this concrete, let&...

....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&qu...

...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...

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