similar to: lme, spline

Hi there, I am reading the 2004 paper "Smoothing with mixed model software" in Journal of Statistical Software, by Ngo and Wand. I tried to run their first example in Section 2.1 using R but I had some problems. Here is the code: library(nlme) fossil <- read.table("fossil.dat",header=T) x <- fossil$age y <- 100000*fossil$strontium.ratio knots <-

I'm trying to do some smoothing with lme and am having some difficulty bringing commands over from S-PLUS to R. I have the following setup (modified from Ngo and Wand, 2004): set.seed(1) x <- runif(200) y <- sin(3*pi*x) + rnorm(200)*.4 ## library(splines) z <- ns(x, 4) The following runs without error on S-PLUS f <- lme(y ~ 1, random = pdIdent(~ -1 + z)) But in R I get

Dear all, I am trying to fit a model with crossed random effects using lme. In this experiment, I have been measuring oxygen consumption (mlmin) in bird nestlings, originating from three different treatments (treat), in a respirometer with 7 different channels (ch). I have also measured body mass (mass) for these birds. id nest treat year mlmin mass ch hack 1EP51711 17

Dear All, I revise my question about the problem I have. Take a look at the article http://www.jstatsoft.org/v09/i01 and download the attached code. try to run one of the codes for example section 2.1 in R here is the code fossil <- read.table("fossil.dat",header=T) x <- fossil$age y <- 100000*fossil$strontium.ratio knots <- seq(94,121,length=25) n <- length(x) X <-

Hello all, In lme4 if you want to model two non-nested random effects you code it like this: mod1 <- lmer(y~x + (1|randomvar1) + (1|randomvar2)) How would you go about to model something similar in nlme? In my database I have two variables for which I have repeated measures, lets call them "individual" and "year". But none of the "individuals" were measured in

Hi, I have a two instance mongrel cluster, running behind apache mod_proxy_balancer on a linux VPS. Mongrel_cluster is 1.01, mongrel is v1.02, rails 1.2.3, ruby 1.8.6. I seem to have enough memory. About 2 minutes after I start the cluster, one of my instances dies - it could be either one. The log file indicates that it received a TERM signal. The instance does die cleanly, removing its pid

Dear R users, Does anyone knows how to run a glmm with one fixed factor and 2 random numeric variables (indices)? Is there any way to force in the model a separate interaction of those random variables with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep

Hi all - R2.0.1, OS X Perhaps while there is some discussion of lme going on..... I am trying to execute a glmm using glmmPQL from the MASS libray, using the example data set from McCullagh and Nelder's (1989, p442) table 14.4 (it happens to be the glmm example for GENSTAT as well). The data are binary, representing mating success (1,0) for crosses between males and females from two

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Hello, I am using read.table to read files directly from a public ftp site. I have a general list of files that may or may not exist in the ftp directory, but my hope was that R would read the file if it existed and ignored it if it didn't exist and move on to the next one. However, when R arrives at a file that does not exist I get the error message "Error in file(file, "rt") :

My experience is very limited on Unix and Samba. Please forgive me if the answer is right in front of me. Problems accessing shares on my Samba 3.0.1pre1 running on Gentoo1.4 I'm stuck big time! I've been trying to figure out why I cannot access shares. The only share I can currently read & write to is: [OpenShare] comment = Temporary file space path = /tmp

Hi all I'm trying to complete a textbook example originally designed for SPSS in R, and I therefore need to find out how to compute an unbalanced ANOVA in R. I did a search on the mailinglist archives an found a post by Prof. Ripley saying one should use the lme function for (among other things) unbalanced ANOVAs, but I have not been able to use this object. My code gives me an error.. Why

Hello, I have an unbalanced mixed model design with two fixed effects "site" (2 levels) and "timeOfDay" (4 levels) and two random effects "day" (3 consecutive days) and "trap" (6 unique traps, 3 per site). The dependent variable is the body length ("BL") of insect larvae from 7 to 29 individuals per trap (104 individuals in total). To account

Full_Name: Simon Frost Version: 1.0.0 OS: Windows Submission from: (NULL) (129.215.60.79) Hi everyone, I've just installed R for Windows - I've previously been using it on Linux - and I've run into a problem when using lme. The lme routine throws up an error in the getGroups.data.frame command, complaining about an error in the formula: Error in getGroups.data.frame(dataMix,

Hola! I am encountering the following problem, in a multilevel analysis, using glmmPQL from MASS. This occurs with bothj rw1062 and r-devel, respectively with nlme versions 3.1-38 and 3.1-39 (windows XP). > S817.mod1 <- glmmPQL( S817 ~ MIEMBROScat+S901+S902A+S923+URBRUR+REGION+ + S102+S103+S106A+S108+S110A+S109A+S202+S401+S557A+S557B+ + YHOGFcat,

On page 165 of Mixed-Effects Models in S and S-Plus by Pinheiro and Bates there is an example of using lme() in the nlme package to fit a model with crossed random factors. The example assumes though that the data is grouped. Is it possible to use lme() to fit crossed random factors when the data is not grouped? E.g., y <- rnorm(12); a=gl(4,1,12); b=gl(3,4,12). Can I fit an additive model

Hi there, I have been trying to fit an NLME to my data. My dataset has two category levels - one is a fixed effect (level1) and one is a random effect (level2), though so far I have only experimented with the highest level grouping (fixed, level1), with the following code: mod1 <- nlme(H ~ a*(1-exp(-b*D^c)), data=sizes, fixed=a+b+c~factor(Loc), start=c(a=75,b=0.05,c=0.7)) This returns the

I am trying to fit lme using R 2.1.1 under Windows 2k. I am getting the following message noted below. Any suggestions that would help me correct my error would be greatly appreciated. Thanks, John > fit1lme<-lme(Velocity~time,data=gate) Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics

Dear all, I am trying to fit a mixed-effects non linear regression, but I have some trouble with it. My data are a balanced panel of 904 subjects with 8 observations (at regular periods) per subject. The functional form of my model is Y=Aexp(-BX1)X2 +e. I want to allow parameters A and B to vary among subjects and also include an autocorrelation term. I have already fitted a standard nonlinear

I want to specify two factors and their interaction as random effects using the function lme(). This works okay when I specify these terms using the function Error() within the function aov(), but I can't get the same model fitted using lme(). The code below illustrates the problem. a <- factor(rep(c(1:3), each = 27)) b <- factor(rep(rep(c(1:3), each = 9), times = 3)) c <-