similar to: Transforming matrix

Dear All, We are about to purchase the second edition of Agresti's "Categorical Data Analysis" (my old copy of the first ed. of that wonderful book is falling apart). I would appreciate suggestions about other comparable books which, if possible, have examples using R/S code (instead of SAS). Thanks, Ram?n -- Ram?n D?az-Uriarte Bioinformatics Unit Centro Nacional de

Hi all, I would like to do some QSAR analysis (quantitative structure activity relationship). I need to use some Partial Least Squares (PLS) regression, but I have not seen this option on the R-project. Is it possible to do this kind of regression on R? thank you in advance best regards, olivier [[alternate HTML version deleted]]

Ich habe in R eine Version von SIR gefunden und ausprobiert. Leider kann diese multivariate Responses nicht verarbeiten. Gibt es in R eine ausgefeilte Version von SIR?

To avoid angry replies, let me first say that I know that the use of Type III sums of squares is controversial, and that some statisticians recommend instead that significance be judged using the non-marginal terms in the ANOVA. However, given that type III SS is also demanded by some… is there a function (equivalent to drop1 for lm) to obtain type III sums of squares for mixed models using the

To anyone who can help, I have two stupid questions, and one fairly intelligent question Stupid question (1): is there an R function to calculate a factorial of a number? That is...is there a function g(.) such that g(3) = 6, g(4) = 24, g(6) = 720, etc? Stupid question (2): how do you extract the estimated covariance matrix of the random effects in an lme object? Intelligent question

Dear All, I have been using the randomForest package for a couple of difficult prediction problems (which also share p >> n). The performance is good, but since all the variables in the data set are used, interpretation of what is going on is not easy, even after looking at variable importance as produced by the randomForest run. I have tried a simple "variable selection"

If an array has missing values in different rows, plotting using the formul= a=20 interface can produce errors. Example: fake.data <- matrix(rep(-100:100, 4), ncol =3D 4) par(mfrow =3D c(1,2)) boxplot(fake.data ~ col(fake.data)) abline(h =3D 0, lty =3D 2) boxplot(as.data.frame(fake.data)) abline(h =3D 0, lty =3D 2) ##### Add the missing data fake.data[190:200, 1] <-

Hi, I looked for the nls() function and couldn't find it. In the refman.pdf it's mentioned as part of the nls package which I cannot find and I also read something about the MASS package, also impossible to find for me. Kris -- ------------------------------------------------------------------------ http://perswww.kuleuven.ac.be/~u0027178/VCard/mycard.php?name=krisn

Hi, I have a microarray matrix 3000x70 for example (genes x experiments). This data has more than 2 classes. I would like to make some filtering on the genes. For example, I would like to exclude genes with the same mean expression across all classes. This is a problem of ANOVA H0:mu1=mu2=...=muk H1: at least one is different First question: Do you think Anova is reasonable for this? Second

Sorry for the stupid question, but is there the factorial function in R? I tried to find it using help.search('factorial') but got nothing appropriate. Many thanks, -Serge

How can I specify the maximum number of iterations in coxph whilst also specifying my model?? I can't find any on-line examples. Thanks

Hello, I'm trying to write code for a repeated measures ANOVA. To put things in perspective, I'll describe my experiment (briefly). I have a 2X2 factorial design with pH (5.5, 6.5) and local community (present, absent) as my treatments. I had plastic enclosures that I sampled across five weeks with the density of 7 species acting as my response variables. I'm analyzing one

Hi All, Sorry if this is a repost (a quick browse didn't give me the answer). I wonder if there are packages that can do the feature selection and classification at the same time. For instance, I am using SVM to classify my samples, but it's easy to get overfitted if using all of the features. Thus, it is necessary to select "good" features to build an optimum hyperplane (?).

How do I cite a package (not R itself - I know how to do that)? Any thoughts or links? Many thanks in advance? Hank Stevens Dr. Martin Henry H. Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/botany/bot/henry.html http://www.muohio.edu/ecology/

I'am a very fresh R user and I'd like to know how I could create such structures. I saw R was objects-oriented but I can not find any doccumentation on about how to build my hown ojects. Thanks.

I have a question for our European readers: how common is it to use commas as decimal points in spread-sheet and statistics applications? Is it an inconvenience to require that all data use a period as decimal point? (i.e., 3.14159 rather than 3,14159). We're trying to make our program as foolproof as possible, and would prefer not to give users a chance to have commas as both decimal

Dear All, I have a list, where several components are NULL, and I'd like to obtain that very same list without the NULL components (i.e., I do not want to unlist or otherwise loose the rest of the list structure). I can do that with a loop, but how could I do it without a loop? Thanks, Ram?n -- Ram?n D?az-Uriarte Bioinformatics Unit Centro Nacional de Investigaciones Oncol?gicas (CNIO)

Hi all, and thx a lot for your answers, in fact i know that R deals with logical numbers 0 and 1 the same way like TRUE and FALSE but my problem is in fact with C language. I have done an interface with C and my first problem was with float variables, but this warning doesn't appear everytime and really i don't know the problem with R: "Warning message: DLL attempted to change

Hi everyone another documented feature that was a bit unexpected for me: R> x <- 19 R> f <- function(t){t+x} R> f(100) [1] 119 --as expected: x is visible from within f() ..but... R> g <- function(a){x <- 1e99 ; return(f(a))} R> g(4) [1] 23 --the "x" that is visible from within g() is just 19, which is not the one I expected it to find. R> rm(x)

Dear all, I am trying to include confidence intervals in a xyplot. This is what I am doing: xyplot(x ~ y|z, alpha = 1,band = "global",panel = panel.locfit) (more specifically, in my case x is a binary response from a logistic regression model) The output plot was fine but it did not include the confidence intervals Anyone knows how to do it? (xYplot did not work either) many thanks