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Hi, Am I hitting some limit in match? Consider the following example: > tst<-seq(100,125,by=.2)%in%seq(0,800,by=.1) > sum(tst) [1] 76 > seq(100,125,by=.2) [1] 100.0 100.2 100.4 100.6 100.8 101.0 101.2 101.4 101.6 101.8 102.0 102.2 [13] 102.4 102.6 102.8 103.0 103.2 103.4 103.6 103.8 104.0 104.2 104.4 104.6 [25] 104.8 105.0 105.2 105.4 105.6 105.8 106.0 106.2 106.4 106.6 106.8

Hi, Am I hitting some limit in match? Consider the following example: > tst<-seq(100,125,by=.2)%in%seq(0,800,by=.1) > sum(tst) [1] 76 > seq(100,125,by=.2) [1] 100.0 100.2 100.4 100.6 100.8 101.0 101.2 101.4 101.6 101.8 102.0 102.2 [13] 102.4 102.6 102.8 103.0 103.2 103.4 103.6 103.8 104.0 104.2 104.4 104.6 [25] 104.8 105.0 105.2 105.4 105.6 105.8 106.0 106.2 106.4 106.6 106.8

Hi, I would also be in favor of a stronger stance on licenses. In industry, where we can really get in big trouble for violating a license, we often maintain internal repositories, or need to be careful about filtering what is used from CRAN. I think that is should really be a requirement the package authors commit to stating what the restrictions are on their packages. Nicholas On 10

Following up to some extent on Friday's discussion regarding the 'validation' of R, could I ask the list group's opinion on possible advantages of R over Splus from a pharma/devices perspective? I wish to exclude the obvious price difference, which doesn’t seem to carry as much weight as I would have thought. Besides, I have noticed many former Splus users gravitating towards R,

Hi, I am working on a study where we need to predict individual survival curves from a cox-model fit with sampling weights. In both R and Splus the survfit.coxph code starts with if(!is.null((object$call)$weights)) stop("Survfit cannot (yet) compute the result for a weighted model") My question is does anyone have code to get the expected survival curve, or even just the base

Hi, I am trying to use the rpart library with my own set of functions on a survival object. I get an immeadiate segmentation fault when i try calling rpart with my list of functions. I get the same problem with the logrank example from Therneau,s S-rpart library though their anova example works. Should I report this as a bug, as even if my functions are structured improperly, that should lead to

Hi I am using R 1.7.1 on RH linux 9.0 > sum(unlist(lapply(ls(),function(x)object.size(get(x)))))/1024^2 [1] 2.424263 so I am not using much memory (I have a gig of ram on my machine) now in nlme > gtest<-groupedData(log(X8)~Time|sub,all[,c(names(all)[1:9],"X8")],outer=~A*B) > object.size(gtest)/1024 [1] 59.98438 > plot(gtest,outer=~Dose*chem,key=FALSE,asp=.5) Plotting

Hi, Before I write code to do it does anyone know of code for fitting mixtures of multivariate-t distributions. I can't use McLachan's EMMIX code because the license is "For non commercial use only". I checked, mclust and flexmix but both only do Gaussian. Thanks Nicholas

Hi, I had some excel files that I needed to read into R, there were alot so I didn't want to do them by hand in gnumeric. I tried all the recommendations in the data import/export manuals, including RODBC, and for some reason they all failed on these files. Then I stumbled on ssconvert, a script that wraps all gnumerics converters, see

Hi the following example I think demonstrates the inconsistency > f<-function(x) x > length(f) [1] 1 > is.na(f) [1] FALSE Warning message: In is.na(f) : is.na() applied to non-(list or vector) of type 'closure' The documentation states: Arguments x an R object to be tested. value a suitable index vector for use with x. And nowhere in the details was it implied (to me

Hi, I am not sure this is a bug but I can repeat it, The functions and data are below. I know this is nasty data, and it is very questionable whether a 4pl model is appropriate, but it is data fed to an automated tool and I would have hoped for an error. Does this repeat for anyone else? My details: > version _ platform i686-pc-linux-gnu

Hi, I guess this is a final plea, and maybe this should go to R-help but here goes. I am writing a set of functions for calibration and prediction, and to calculate standard errors and intervals I need the variance function to be evaluated at new prediction points. So for instance fit<-gnls(Y~SSlogis(foo,Asym,xmid,scal),weights=varPower())

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to

Hi, The move to sRGB is nice, is there any interest in adding an interface to lcms, http://www.littlecms.com, to allow gamut matching? I can think of a lot of instances where I would like to render a figure as it would appear on my printer. This is probably best done as a separate package though, at least at first. Nicholas Martin Maechler wrote: >>>>>> "Paul" ==

Hi, I'm trying to plot a longitudinal data set, using ggplot and adding some summary info (eg. mean, 1 sd bounds) using geom=ribbon. The summary info is based on a subset of the original data (eg. less an outlier). But I'm having trouble getting the ribbons to show up correctly. It's probably something obvious that I'm missing as a novice at ggplot2, and any help is much

Hi, I am writing some basic smoothers in R for cleaning some spectral data. I wanted to see if I could get close to matlab for speed, so I was trying to compare SparseM with Matrix to see which could do the choleski decomposition the fastest. Here is the function using SparseM difsm <- function(y, lambda, d){ # Smoothing with a finite difference penalty # y: signal to be smoothed #

Hi, consider the following > a<-gl(3,3,9) > a [1] 1 1 1 2 2 2 3 3 3 Levels: 1 2 3 > levels(a)<-3:1 > a [1] 3 3 3 2 2 2 1 1 1 Levels: 3 2 1 > a<-gl(3,3,9) > factor(a,levels=3:1) [1] 1 1 1 2 2 2 3 3 3 Levels: 3 2 1 It is probably something obvious I missed, but reading the documentation of factor, and levels I would have thought that both should produce the same output as

Hi, I am having trouble debugging this one. The code is attached below, but it seems to be a problem at the C-tk interface. If I run this 1 time there are no problems if I run it more than once I start to get warnings that increase in multiples of 11 everytime I run it. Here is a sample session > source("clrramp2.r") Loading required package: tcltk Loading Tcl/Tk interface ... done