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Peter Dalgaard writes: > Ross Ihaka <ihaka at stat.auckland.ac.nz> writes: > > Many of the incompatibilities result from us not being familiar with > > some of the inner mysteries of S - these are generally pretty easy to > > fix. Some incompatibilities however result from the fact that R > > started life as a kind of Lisp interpreter. These can be quite a bit

Dear R collegues, I'm trying to understand what's AIC in R (ver. 1.3.1), and I'm getting a different answer if I look at the AIC(of the fitted model) or the aic in the summary( of the fitted model). Is this correct? Can somebody explain me the difference between the two values? or Is the AIC criterion not appropiated for Poisson models? R session: > t1 <- glm(tax ~ areal,

Dear R collegues, I'm trying to understand what's AIC in R (ver. 1.3.1), and I'm getting a different answer if I look at the AIC(of the fitted model) or the aic in the summary( of the fitted model). Is this correct? Can somebody explain me the difference between the two values? or Is the AIC criterion not appropiated for Poisson models? R session: > t1 <- glm(tax ~ areal,

Dear all, I have some trouble using the "id"-argument with aftreg (accelerated failure time regression analysis from the eha library). As far as I understand it, the id argument is used to group individuals together if there are time-varying covariates and the data is arranged in counting process style. Unfortunately, i cannot figure out how to use the "id"-argument. The

Hello I am searching ghyper package (generalized hypergeometric distributions). Does anyone can send it to me? Regards from Mexico Lizbeth Román [[alternative HTML version deleted]]

I have to estimate the following model for several group of observations : y(1-y) = p[1]*(x^2-y) + p[2]*y*(x-1) + p[3]*(x-y) with constraints : p[1]+p[3] >= 1 p[1]+p[2]+p[3]+1 >= 0 p[3] >= 0 I use the following code : func <- sum((y(1-y) - p[1]*(x^2-y) + p[2]*y*(x-1) + p[3]*(x-y))^2) estim <- optim( c(1,0,0),func, method="L-BFGS-B" , lower=c(1-p[3], -p[1]-p[3]-1,

Hi, i have two files (file1.txt and file2.txt) which i would like to merge, based on certain criteria, i.e. it combines data based on matching geneID and exons. i have used the merge option, but it does not give me the desired outcome. merged.txt shows the result i would like. *File1. txt* ** AffyProbe ProbeType Flag GeneSymbol GeneID Exons Chrom Strand Affytart AffyEnd 1

...ds the local user database to incorporate the user database on the Windows box, for that service (ssh). Correct? And the same principle could be applied to any other PAM-enabled account database, for ssh or other services, yes? ********** Assertion 3 ********** This relates to the difference in _function_, if any, between the list of "Account Information Databases" provided in Chapter 10 of the Official Samba-3 HOWTO: plain text smbpasswd ldapsam_compat tdbsam ldapsam mysqlsam xmlsam and the list in "PAM-based distributed authentication" provided in Chapter 24 of the Official S...

With the approach of R 2.12.0: with mild apologies for re-opening this perennial issue: is there any hope, if appropriate patches are submitted, of adding a drop.levels argument (with default equal to FALSE to preserve backward compatibility/efficiency) to the subset function ... ? If not, would a patch to the documentation and/or the R FAQ be accepted? This does seem to be a continuing

i believe the following is true but would appreciate confirmation that it is intended behavior and will continue: if a default argument is employed in the definition of a generic function, and the generic is called with the argument in question (call it 'ARG') missing, then the method for signature (..., ARG = "missing", ...) will be called by 'standardGeneric'

I apologize for posting this in essence the second time (no light at the end of the tunnel yet..): is there a way to enforce that "nls" takes both, the data *and* the model definition from the parent environment? the following fragment shows the problem. #======== cut here========== wrapper <- function (choose=0) { x <- seq(0,2*pi,len=100) y <- sin(1.5*x); y <-