similar to: How to specify arguments in lme() ?

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,

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

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

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

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 have a problem running this program on R. Z is a matrix of spline which is random > fit<-lme(anc~X,random=pdIdent(~Z)) Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups What I have done wrong?

I have an ANOVA model with 2 factors "Environment" and "Site", "Diameter" is the response variable. Site should be nested within Environment. Site is also a random factor while Environment is fixed. I can do this analysis using the "aov" function by using these commands: >model<-aov(Diam~Env+Error(Env%in%Site),data=environ) >summary(model)

Hi everone, I have a question on using lme on a mixed effects model. The linear mixed model is in the form of: y = bX +Zu + e where "X" and "Z" are the matrices, "b" is the coefficient vector of fixed effects, "u" is the coefficient vector of random effects, and e is an error vector. I would like to use "lme" function to fit the model and

Hello, I want to perform an lme on a database with this structure: ID Sequence Temperature Tumour Error 1 5 0 1 8.721872e-08 1 5 0 2 8.695348e-08 1 5 0 3 2.019604e-13 1 5 37 1

Hi all, I have collected response time data from 178 participants ('sub') for each combination of 4 within-Ss factors ('con','int','tone','cue'). Additionally, I have recorded the gender of each participant, so this forms a between-Ss factor ('gender'). Normally this would be analyzed using aov:

Classification: UNCLASSIFIED Caveats: NONE Dear R users, I'm interested in finding a random effect of the Block in the data shown below, but 'lme' does not work without the random effect. I'm not sure how to group the data without continuous value which is shown in the error message at the bottom line. If I use 'aov' with Error(Block), is there a test method comparing

Dear list, the following should fit the model log(PEPC.Wert)=fKTemp+fHerk+interaction(fKTemp,fHerk)+fMub+error, where fKTemp, fHerk are fixed effect factors and fMub is a random effects factor nested in fHerk (values are different fopr different values of fHerk). > logpepcr1 <- lme(log(PEPC.Wert) ~ fKTemp*fHerk, random= ~ 1 | fMub, na.action=na.omit) The following should be without the

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

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

Windows XP R 2.10 I am trying to run lme and get the following error: > fitRandom <- lme(values ~ subject, + data=withindata) Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups my data follows, below which is a copy of all my code > > print(withindata) subject values 1 1 2.3199639 2 1 -8.5795802 3 1 -4.1901241 4

Hi, I am working with R version 2.1.0, and I seem to have run into what looks like a bug. I get the same error message when I run R on Windows as well as when I run it on Linux. When I call anova to do a LR test from inside a function, I get an error. The same call works outside of a function. It appears to not find the right environment when called from inside a function. I have provided

Dear R-Community! For example I have a study with 4 treatment groups (10 subjects per group) and 4 visits. Additionally, the gender is taken into account. I think - and hope this is a goog idea (!) - this data can be analysed using lme as below. In a balanced design everything is fine, but in an unbalanced design there are differences depending on fitting y~visit*treat*gender or

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

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

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 <-