similar to: ordering levels

Sorry, sorry.... of course levels(testf)[c(2,1,3)] will do the job My excuses to all Anne PS I will meditate the following saying "la parole est d'argent et le silence est d'or" BONNES FETES A TOUS SEASONAL GREETINGS ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com

I was wrong about needing the Relevel from the Lexis package. The default verson of relevel does the job of reshuffling levels in any desired order, albeit with a warning (which comes from the fact that apparently only a single number had been anticipated by the designer): > testf <- factor( sample( letters[1:4], 100, replace=T ) ) > table( testf, newf=relevel( testf, ref=c(3,2,1,4) )

To the R developers, The discussion below reminded me that I think it might be a good idea to take the Relevel function from the Lexis package and replace relevel in stats with it. This is really nothing special for epidemiology. It is fully compatible with the existing relevel (it actually contains the relevel code almost verbatim as a subset), but it has the extra functionality of combining

Hello R helpers! I looked but did not find a table-lookup R-utility. I could use a loop to do the job (old FORTRAN/C habits die hard) but if I have a big table in which I have to search for the values corresponding to a vector, I end up logically with a double loop. Is there already such a utility? Otherwise, is there a way without loops? Thanks as always Anne

hello to all! I have a small problem wit predict() for lm Let's say I have predictors x1 and x2, response y I want to predict for a new ds say dn<-data.frame(x1= seq(min(x1),max(x1),length=10),x2=rep(median(x2),10)) predict(lm(y~x1+x2),dn,se.fit=T) Error message > Error: variables 'x1', 'x2' were specified differently from the fit (I looked in the help and found

Is there an upgrate of the vcd library (visualisation of categorical data) for the latest R version? Trying to download it from CRAN I get URL /data/WWW/ftp/pub/R/bin/windows/contrib/r-release/vcd_0.1-3.4.zip was not found on this server. googling it, I found it for instance on http://www.sourcekeg.co.uk/cran/bin/windows/contrib/1.9/ but trying to install it gave me the message >

Hi! I'm trying to do some ploytomous logistic regression using multinom() in the nnet package, but am a bit confused about interpretation of the results Is it possible to get the following quantities: I: maximum likelihood estimates to test for fit of model and significance of each predictor (I would like to produce a table of the following type) Analysis of Variance: MLE (values are

Hi all, I still have problems with the predict function by setting up the values on which I want to predict ie: original df: p1 (193 obs) variates y x1 x2 rm(list=ls()) x1<-rnorm(193) x2<-runif(193,-5,5) y<-rnorm(193)+x1+x2 p1<-as.data.frame(cbind(y,x1,x2)) p1 y x1 x2 1 -0.6056448 -0.1113607 -0.5859728 2 -4.2841793 -1.0432688 -3.3116807 ...... 192

Hi! Is there an easy way to add to the scree-plot labels to each value pertaining to the cumulative proportion of explained variance? Thanks and a happy new year Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com --------------------------------------------------- M-TD Modelling and Technology Development PSE-C

Hello R helpers! I'm using the avas function form package acepack (called from areg.boot package Hmisc) to estimate automatically transformations of predictors (in this case monotonous) and response. Well, it seems to work quite well, but I have 3 basic questions: - which set of basis functions is used in this procedure? - how do I back transform my estimate (y hat ) to the originasl scale?

Dear R-helpers, I have a problem which I suppose is trivila, but... I have included NA values as factors ( (to be able to make nice printed summaries with NAs % ba category ) with the following code dat$x.f<-factor(dat$x, exclude=NULL); levels(dat$x.f)<-c("A1","A2","A3","A4","NA"); length(dat$x.f) Now, I want to impute the missing values.

Hi R helpers! I'm still a bit ( alot) confused by the use of "inverseTrans" and "ytype" in areg.boot (Hmisc): What I want to do seems very simple, but I do not get the result I want: plot the predicted values in the original scale. (I did not understand the documentation, sorry!) for instance the following code

I'm about to present a report (for internal use of governmental agency). I used extensively R , contibuted packages, as well as communications on the R-list As well as citing R, I would like to know how to cite the contributed packages (it is not so easy, as some have been used exensively, other marginally, some are called from another package and some were not used as softwares but gave me

Another newbie question.... I want to create a factor (say cT)from a numerical variable (sy temp) by regrouping the values in classes (say cT <390, [390,400[,[400,409[...>=550) Is there a simple way of doing that using the factor function? AND I do not find how to manipulate strings (I want to concatenete characters strings ("abkdas","chjw") into something like

********************************************************************** This email and any files transmitted with it are confidential and intended solely for the use of the individual or entity to whom they are addressed. If you have received this email in error please notify the system manager. ********************************************************************** Hi Just installing R and some

What R functionnalities are there to do missing values imputation (substantial proportion of missing data)? I would prefer to use maximum likelihood methods ; is the EM algorithm implemented? in which package? Thanks Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com

This is quite strange behaviour - at least for R-novice as myself.... Consider this: > testf <- function() { x <-2; sprintf("%s %f", "x =", x); return(x) } > result <- testf() > testf <- function() { x <-2; sprintf("%s %f", "x =", x) } > result <- testf() > testf() [1] "x = 2.000000" Apparently adding return()

I am using the following model: lm <- lm(mydata[[variableName]] ~ Age + Gender + Group, data=mydata) There are 5 groups in "Group": nonc (the control), c1,c2,c3 and c4. How do I contrast nonc vs the others? and How do I contrast c1 vs other c's (ie c2,c3,c4 as a subgroup)? I have looked at the contrasts option in lm and model.matrix and am really none the wiser. Though it

Dear R helpers, I am having trouble with an array inside a loop. I wish to accumulate the results of a function in an array within the function, over several loops of a program outside the function. The problem is that the array seems to re-set at every entry to the function. Here is an example, so you can see what I mean.

I have a question with llvm-gcc and llvm-gfortran. Is it possible that llvm-ld can combine different bc files compiled from C programme and Fortran Programme together? For example: Compile a c program into llvm bc file by the task: # llvm-gcc -emit-llvm test.c -c -o test.bc and then compile a fortran program into llvm bc file by the task: # llvm-gfortran -emit-llvm testf.f -c -o testf.bc And