similar to: Small typo in An Introduction to R (PR#1402)

The core team has decided to release R 1.5.0 on April 29th. Somewhat earlier than maybe expected, but we realized that we needed a phase-shift away from our usual cycle with main releases in June and December since it was placing feature freezes just when several members were in a creative phase due to end of teaching. The roadmap is as follows April 8 feature freeze on r-base April 15 code

Respected Sir/Madam, I have a question regarding calculation of LD50 (Lethal Dose) and IC50 (50% inhibitory concentration) of an antimicrobial experiment. I have used a compound isolated from a plant and observed its effect on the fungus *Fusarium oxysporum* by the food poisoning method. Solutions of the compound at concentrations of 0, 50, 100, 150, 200 and 250µg/ ml were added to

I downloaded the latest version of R (1.5.0) and issued a read.table() command that used to work with (1.3.1). Unfortunately I erased 1.3.1 from my hard drive after I downloaded R 1.5.0. Here's the command and the error message I get under 1.5.0: > golub1<-read.table("C:/Microarrays/code/data_set_ALL_AML_train.txt",sep="\t",q

Is there an implementation of a reasonable k-nearest neighbor finder already in one of the packages? -- +---------------------------------------------------------------------------+ | Robert Gentleman phone : (617) 632-5250 | | Associate Professor fax: (617) 632-2444 | | Department of Biostatistics office: not yet

Hello, I thought some of you might like to know about a GNU project that is complementary to R in some ways, namely gretl http://ricardo.ecn.wfu.edu/gretl (GNU Regression, Econometrics and Time-series Library). gretl (a library with cli and gui clients, the gui using GTK) is designed to be very user-friendly, and suitable for teaching econometrics. It has a fairly wide variety of least-squares

Hi, I am having trouble with the survival library, particualrily the coxph function. the following works coxph(jtree9$cph.call,z,rep(1,dim(z)[1])) Call: coxph(formula = jtree9$cph.call, data = z, weights = rep(1, dim(z)[1])) coef exp(coef) se(coef) z p SM 0.2574 1.294 0.0786 3.274 1.1e-03 Sex -0.1283 0.880 0.1809 -0.709

I'm trying to do a Windows build of Ray Brownrigg's port of the maps library, and running into some problems. There's a makefile for the shared library. I suspect that I just need to change that to get it right, and everything will build properly, but I'm running into trouble. Here's the original makefile, along with my comments about changes: >SRC= mapget.c mapproject.c

I'm trying to do a Windows build of Ray Brownrigg's port of the maps library, and running into some problems. There's a makefile for the shared library. I suspect that I just need to change that to get it right, and everything will build properly, but I'm running into trouble. Here's the original makefile, along with my comments about changes: >SRC= mapget.c mapproject.c

I tend to use table() quite a bit for quick "diagnostics", summary, etc. I have wished for a more automatic way of plotting these. One possibility would be something like the following function; The question is if (something like) the following is worth providing (and then maintaining...) at all : plot.table <- function(x, type = "h", ylim = c(0, max(x)), lwd = 2,

The point estimates produced by wilcox.test are perverse (not wrong, just brain damaged). The Hodges-Lehmann estimator that goes with the signed rank test is the median of the Walsh averages. The Hodges-Lehmann estimator that goes with the rank sum test is the median of the pairwise differences. wilcox.test agrees except that it uses the following very peculiar definition of "sample

Hi, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for females using the dose.p() command in the MASS

I'm trying to build the 1.4.0 development version of R for Windows. I'll be posting problems as they arise, until I give up (or succeed), or someone asks me to stop. 1. I don't have zlib, the compression library. The source file gnuwin32/unzip/WHERE says to get it from ftp://ftp.info-zip.org/pub/infozip/zlib/zlib.html; that web site says it has been closed and redirects me to

I just updated my web server box, running Debian testing, to samba v3, and am having some problems with a share. Can anyone advise me on this? I have a share called "allsrvr" which is defined in my smb.conf file as follows. Please note that security isn't an issue here. The server is connected to my home system via a VPN and all smb/cifs ports are closed to the world at the

I have always avoided missing data by keeping my distance from the real world. But I have a student who is doing a study of real patients. We're trying to test regression models using multiple imputation. We did the following (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of

thanks a lot Renaud. but i was interested in Finney's fiducial confidence intervals of LD50 so to obtain comparable results with SPSS. But your reply leads me to the next question: does anybody know what is the best method (asymptotic, bootstrap etc.) for calculating confidence intervals of LD50? i could "get rid" of Finney's fiducial confidence intervals but

Hi All! I am desperately needing some help figuring out how to calculate LD50 with a GLMM (probit link) or, more importantly, the standard error of the LD50. I conducted a cold temperature experiment and am trying to assess after how long 50% of the insects had died (I had 3 different instars (non significant fixed effect) and several different blocks (I did 4 replicates at a time)=

Dear all I need to obtain an estimate of the 50% lethal dose (LD50) from a logistic regression model obtained by applying the glm procedure to some binomial data. The model appears to fit the data very well. I used dope.p from MASS to try and find LD50. The following output appears: > dose.p(iso.glm.logit, cf = c(1,3), p = 1:3/4) Error in dose.p(iso.glm.logit, cf = c(1, 3), p = 1:3/4) :

Hi Duncan, I've been looking around at GUI toolkits to use with Python (and by extension R). I initially tried Tk, but was disappointed because there is no consistent set of high level widgets. I've subsequently done some looking at QT, wxWindows, and GTK. QT is out because I am a *commercial* user, and I don't want to have a license hassle. GTK seemed to have a lower level of

Hi, Kurt Hornik and I will start to publish a quarterly electronic ``R Newsletter'' beginning with January 2001 (if all goes well). The idea is to have a mixture of articles describing 1) new features in R itself 2) contributed add-on packages 3) nice applications 4) more general statistical computing issues related to R 5) or ... OK, now comes my christmas wish to you: If you are

I am unfamiliar with R and I’m trying to do few statistical things like GLM and GAM with it. I hope my following questions will be clear enough: My datas ( y(i,j ))are run off triangles for example : J=1 J=2 J=3 I=1 1 2 3 I=2 4 5 I=3 6 My model is : E[y(i,j)] =m(i,j) Var[y(i,j)] =constant *m(i,j) Log(m(i,j)) = eta (i,j) eta (i,j) = c + alpha(i)