similar to: lme formula

I'm an user of R for Windows (95/NT PENTIUM I and II) mostly for educational purposes. I thinking of moving to Linux, is there any Linux distribution in which R works better? or it would have to work fine in any Linux distribution? Thanks for any hint ************************************************************* * Eusebio Arenal Guti?rrez * * Departamento

Is there any documentation about replacement and extraction functions in R, these fuenctions don't work as in S. Example with funtion factor.names (of S+ 4.5r2) > fnombres <- list(tratamiento=LETTERS[1:4], veneno=c("I","II","III")) > veneno.diseno <- data.frame( + factor(rep(c(1,2,3,4), rep(12,4))), + factor(rep(rep(c(1,2,3), rep(4,3)),4)) + ) >

I want to fit the following model: > aov.madera <- aov(tiempo~producto/panel, data = madera) > anova(aov.madera) Analysis of Variance Table Response: tiempo Df Sum Sq Mean Sq F value Pr(>F) producto 2 93.631 46.815 63.0511 4.304e-07 *** producto:panel 9 43.533 4.837 6.5144 0.001881 ** Residuals 12 8.910 0.742 ---

I'm a beginer programming C and I have the following problem: I have the following C-code file #include <stdlib.h> void gen(int *n, int *a, int *c, int *m, int *x0, int *x); main(){ int nn = 31; int aa = 3; int cc = 0; int mm = 31; int xx0 = 9; int xx[nn]; int i; gen(&nn,&aa,&cc,&mm,&xx0,xx); for (i = 0; i <= nn-1; i++) printf("%d

It is posible to change the console font size? What in the funtion needed? Tanks in advance Eusebio -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at

After installing R 0.99.0a on a Windows 95 Rgui.exe crash after opening the console window. Rterm.exe works fine. Any hint? It is posible that R is not compatible with windows configuration modified by some other program? Where can I act? Error message: Rgui causes an exception 104 on module R.dll of 0137:100324d4 records EDX=0434cfd8 CS=0137 EIP=100324d4 EFLGS=00010206 EBX=815ce8e4 SS=013f

I've just installed R for windows 1.7.0 on windows 2000 SP2 from the binaries on CRAN. It takes at least 1:30 minutes to completely open Rgui, Rterm o R in Emacs. It this normal? Data about the platform: C:\>ver Microsoft Windows 2000 [Versi?n 5.00.2195] > Sys.info() sysname release "Windows"

Dear list, I am faced with the following model: y=E+P+M+H+PxE+Error y is a response, E and P are factors with fixed effects. M is a random effect nested in P and H is a random effect nested in M. PxE is interaction of P and E. It seems that I should fit such a model with the lme function of library nlme, but I was not able to figure out from the help page how to specify the formula. In the

Using formulas constructed from strings only partially works for me in lme: library(nlme) data(Orthodont) fm2<-lme(as.formula("distance~age"),data=Orthodont,random=~1|Subject) summary(fm2) # works augPred(fm2) # fails #Error in inherits(object, "formula") : #Argument "object" is missing, with no default I assume that my use of as.formula is wrong, but

Dear R-Users, I am currently working with LME to analyse repeated measures data. I encounter a problem when including both a random effect and a correlation structure with different grouping levels into the LME model. The error message is: Error in lme.formula(diameter ~ flowers*timef + competition*timef + population*timef, : Incompatible formulas for groups in "random" and

Using a formula converted with as.formula with lme leads to an error message. Same works ok with lm, and with lme and a fixed formula. # demonstrates problems with lme and as.formula demo<-data.frame(x=1:20,y=(1:20)+rnorm(20),subj=as.factor(rep(1:2,10))) demo.lm1<-lme(y~x,data=demo,random=~1|subj) print(summary(demo.lm1)) newframe<-data.frame(x=1:5,subj=rep(1,5))

This is a continuing issue with the one on the list a long time ago (I couldn't find a solution to it from the web): -------------------------------------------------------------------------- > Using a formula converted with as.formula with lme leads > to an error message. Same works ok with lm, and with > lme and a fixed formula. > > # demonstrates problems with lme and

I'm a beginner in R, and trying to fit linear models with different intercepts per group, of the type y ~ A*x1 + B, where x1 is a numerical variable. I cannot understand whether I should use y1 ~ x1 +1 or y1 ~ order(x1) + 1 Although in the toy example included it makes a small difference, in models with many groups the models without order() converge slower if at all!

Kia ora I'm a using lme (from nlme package) with data similar to the Orthodont dataset and am getting positive log-likelihoods (>100). This seems usual and I wondered if someone could offer a possible explanation. I can supply a sample dataset if requested, but I feel almost certain that this question has been asked and answered recently. However, I can find no trace of it in the mail

I have the same question that Eusebio had: Is there a function similar to "sink" that redirect also R code to a file that is: sink("R001") x <- c(2,-6,-4,8,5,4,1,3,4,-9,0,1) A <- matrix(x, ncol=3) A A.prima <- t(A) A.prima dim(A) dim(A.prima) sink() create a file "R001" with contents: ------------------------------------------ [,1] [,2] [,3] [1,]

Dear all, I am quite new at R and have a question about using lme with crossed random factors. I followed the instructions of Pinheiro & Bates, but that did not work because of the non grouping of my data. Reading prior threads ( http://www.mail-archive.com/r-help@stat.math.ethz.ch/msg10849.html), I found a solution to deal with non grouped data and crossed random factors in lme, by defining

I am using lme to handle repeated measurements. So far i can follow the examples from the book from pinheiro and bates. Now i get the problem , that i have "nested" repeated measuremnts, and i cant find out how to do the grouping part of the lme formula. 1.The simple problem ist that i have different Samples , from which i make repeated measurements (each sample is measured 6 times) and

I am trying to fit a formula to my data, but I just can't find the right way to do it. My experiment consists of manipulating FRUITS and VEGETATION to two levels each(intact or removed) on 12 experimental plots. This leaves me with 4 treatment combinations Fruit intact Vegetation removed Fruit int. Veget int. Fruit rem. Veget rem. Fruit rem. Veget. intac those treatements are distributed

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

Dear R-mixed-effects-modelers, I could not answer this questions with the book by Pinheiro & Bates and did not find anything appropriate in the archives, either ... We are preparing a short lecture on degrees of freedom and would like to show lme's as an example as we often need to work with these. I have a problem in understanding how many dfs are needed if random terms are used for