similar to: Setting plots margin

Hello! After a presentation of some statistical analysis of process datas, (where the few R possibilities I was able to show made quite a big impression), I was asked if it was possible to program a statistical application which could be used directly by the end user. Such an application would include a userfriendly interface (developped in C++), a db , a core statistical program, standard

Hello, I'm new to R and in the process of testing it My first question: I fail to read in my data (ANSI toto.txt file, tab separated) > test <-read.table("toto.txt") Error in file(file, "r") : unable to open connection In addition: Warning message: cannot open file `toto.txt' > test <-

Dear R helpers! Is there an utility for multiple logistic regression where the response is not ordinal? As the predictors do not satisfy the necessary hypothesis, I cannot use discriminant analysis. Thanks and have a nice R-day Anne [[alternative HTML version deleted]]

Hi, I'm very new to R. I am most interested in the variable importance measures that result from randomForest, but many of my predictors are highly correlated. My first question is: 1. do highly correlated variables render variable importance measures in randomForest invalid? and 2. I know that cforest is robust to highly correlated variables, however, I do not have enough space on my

> ---------- Forwarded message ---------- > Date: Mon, 18 Jul 2011 10:17:00 -0700 (PDT) > From: KHOFF <kuphoff at gmail.com> > To: r-help at r-project.org > Subject: [R] cforest - keep.forest = false option? > > Hi, > > I'm very new to R. I am most interested in the variable importance > measures > that result from randomForest, but many of my predictors

********************************************************************** 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

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

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

Hello! I would like to know if there is a simple way to reorder levels of a given factor.Let's say that the vector testf<-factor(c("red","red","red","blue","blue","white")) levels(testf) : blue red white should have reordered levels such as levels(testf) : red blue white (this is for presentation purposes) I guess

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

Hello R-helpers I want to put a color scale in a plot: I've got an xy plot where the values of the response (z=f(x,y)) is symbolically given by colors (like heat or rainbow color scale) I would like to put such a scale with apprpriates labels in the plot, so as to facilitate the interpretation (like in a finite elements result plot) How is taht possible? Thanks Anne [[alternative

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

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

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

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.

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

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?

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

Hello! I would like to add a plot of the regression surface to my cloud plot . Is it possible? Thanks Anne [[alternative HTML version deleted]]