similar to: correlation structure in lme without random effect

Hi, I'm trying to set up AR(1) as a correlation structure in modeling some data (attached file data.txt in text format) with lme, but have trouble getting it to work. Incent, Correctness, and Oppor are 3 categorical variables, Beta is a response variable, and Time is an equally-spaced variable with 6 time points (treated as a categorical variable as well). Basically I want to model the

Hi, To simplify, suppose I have 2 observations each day for three days. I would like to define the correlation structure of these 6 observations as follows: the correlation of 2 observations on the same day is, say, alpha, the correlation for 2 observations one day apart is rho and the correlation for 2 observations 2 days apart is rho^2. I.e. I would like to have an AR1 correlation + a

Hello R-users, I am using gls function in R to fit a model with certain correlation structure. The medol as: fit.a<-gls(y~1,data=test.data,correlation=corAR1(form=~1|aa),method="ML") mu<-summary(fit.a)$coefficient With the toy data I made to test, the estimate of mu is exactly equal to the overall mean of y which can not be true. But, if I make a toy data with y more than two

Dear all, I met with some problems when dealing with a time series with serial correlation. FIRST, I generate a series with correlated errors set.seed(1) x=1:50 y=x+arima.sim(n = 50, list(ar = c(0.47))) SECOND, I estimate three constants (a, b and rho) in the model Y=a+b*X+u, where u=rho*u(-1)+eps library(nlme) gls(y~x,correlation = corAR1(0.5)) # Is it the right procedure?

Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,

Full_Name: Ben Tyner Version: 2.2.0 OS: i686-pc-linux-gnu Submission from: (NULL) (128.210.141.240) My appologies if this has already been fixed, but I didn't see it in the tracking system yet so I thought I'd report it. Demonstration: xyplot(Petal.Length ~ Petal.Width | Species, iris, strip = strip.custom(style = 1, var.name = expression(beta),

I am trying to extract into a scalar the value of Phi from the printed output of gls or lme using corAR1 correlation. ie I want the estimate of the autocorrelation. I can't see how to do this and haven't seen it anywhere in str(model.lme). I can get all the other information - fixed and random effects etc. Is there an obvious way so that I can save the brick wall some damage? TIA

> myfit1 <- gls(col1 ~ col2+col3+col4+col5+col6-1, data=data2, corr=corAR1( 0.3202), method='ML') Error: cannot allocate vector of size 199712 Kb if I get rid of the "corr=corAR1(0.3202)" option, it works okay... can anybody help me? thanks a lot! [[alternative HTML version deleted]]

Background: I'm a student of Prof. Cleveland at Purdue University. Eventually, we'd like to release a new version of the loess routine in R. For starters, this implementation would have support for local polynomial degree 3, better control over the number of cells in the KD tree, and perhaps a better solution in higher predictor dimension. I see that Prof. Ripley was responsible for

Say I have library(lattice) x<-runif(256) y<-runif(256) f<-gl(16,16) g1<-rep(1:4,each=64) g2<-rep(1:4,times=64) plot<-xyplot(y~x|f, groups=g1, pch=as.character(1:4), panel=function(x,y,subscripts,groups,...){ panel.superpose(x,y,subscripts,groups,...) }) print(plot) Currently, both

Full_Name: Benjamin Tyner Version: 2.3.0 OS: linux-gnu (debian) Submission from: (NULL) (71.98.75.54) > mean(NA) returns -2147483648 on my system, which is -(1+.Machine$integer.max) > sessionInfo() Version 2.3.0 (2006-04-24) i686-pc-linux-gnu attached base packages: [1] "methods" "stats" "graphics" "grDevices" "utils"

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

Dear R-project users I would like to calculate a linear trend versus time taking into account a first order autoregressive process of a single time series (e.g. data$S80 in the following example) using th gls function. gls(S80 ~ tt,data=data,corAR1(value, form, fixed)) My question is what number to set in the position of value within corAR1? Should it be the acf at lag 1? I look forward for

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

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

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

Dear List: A few weeks ago I posted some questions regarding data simulation and received some very helpful comments, thank you. I have modified my code accordingly and have made some progress. However, I now am facing a new challenge along similar lines. I am attempting to simulate 250 datasets and then run the data through a linear model. I use rm() and gc() as I move along to clean up the

Dear all, I am trying to define my own corStruct which is different from the classical one available in nlme. The structure of this correlation is given below. I am wondering to know how to continue with this structure by using specific functions (corMatrix, getCovariate, Initialize,...) in order to get a structure like corAR1, corSymm which will be working for my data. Thanks in advance.

Dear List: First, many thanks to those who offered assistance while I constructed code for the simulation. I think I now have code that resolves most of the issues I encountered with memory. While the code works perfectly for smallish datasets with small sample sizes, it arouses a windows-based error with samples of 5,000 and 250 datasets. The error is a dialogue box with the following: "R

Dear List: I am running into a memory issue that I haven't noticed before. I am running a simulation with all of the code used below. I have increased my memory to 712mb and have a total of 1 gb on my machine. What appears to be happening is I run a simulation where I create 1,000 datasets with a sample size of 100. I then run each dataset through a gls and obtain some estimates. This works