similar to: Am I an idiot?

I'd like to make axis titles, chart titles, legends and the like mathematically nice (e.g., have proper superscripts and subscripts, variables in italics, and so on). Ideally, one could go into ``math mode" a la LaTeX, and write something like plot(blah, xlab="Methane in $\mbox{cm}^3$ / g", blah). This is just an ideal, but it would be nice. Is some approximation of this

Potentially Stupid Question Department: I just tried to load a few packages in a row. First I loaded MASS, then tseries, then nls. A typical error message from the last two looked like this: Attaching Package "package:nls": The following object(s) are masked from package:MASS : profile Is this a big deal? Does it mean that I lose any functionality from the

With Tony Rossini's help we have established anonymous CVS access to a read-only copy of the main R CVS tree at cvs.r-project.org. This would be of interest primarily to those who want to track the development version (and who have access to CVS, of course). To use anonymous cvs you must log in to the server under the name "anoncvs" and with the password "anoncvs". You

With Tony Rossini's help we have established anonymous CVS access to a read-only copy of the main R CVS tree at cvs.r-project.org. This would be of interest primarily to those who want to track the development version (and who have access to CVS, of course). To use anonymous cvs you must log in to the server under the name "anoncvs" and with the password "anoncvs". You

Hello, I recall reading a thread months ago on this mailing list about handling very datasets under R, but I can't seem to find it. This has become particularly important recently, because I've been playing with a dataset containing information about every fatal car accident in the U.S. since 1975; in total, the relevant files are about 120 megs. I'd like to load all of these into R

Hey everyone, Might it be possible for future versions of PC-R to highlight the opening brace in a pair, a la emacs? Programs that do that are infinitely friendlier than those that don't. Thanks, Steve Stephen R. Laniel | "I've got a match: Carnegie Mellon University | Your embrace and my collapse." laniel at cmu.edu | --They Might Be

I have sort of an emergency question for the list. One of my professors for an S-Plus intensive class distributed a function to produce partial regression plots. I need to run it under R, because I'm doing the homework on my home computer with a modem; hence I don't have the speed required to emulate X-Windows and run S Plus off one of the campus servers. Bottom line: I'm using R.

Hi there, I'm using R1.0.1 Windows 98. This file contains some inputs and an aov function code. Can someone check it for me? Somehow I got completely different answer when typing them in R and in Splus. Splus gives me this: > summary( Turnip.aov ) Error: Blocks Df Sum of Sq Mean Sq F Value Pr(F) Residuals 3 163.7367 54.57891 Error: Plots %in% Blocks

At 06:09 AM 5/1/00 +0100, Prof Brian D Ripley wrote: >On Sun, 30 Apr 2000, Stephen R. Laniel wrote: > >> I was just now surprised to note that functions to go generalized additive >> models don't appear to exist under R 1.000. In particular, the gam() and >> loess() functions aren't there. Are they hidden somewhere and I just >> haven't noticed? >

Does anyone know of any comprehensive literature (like a book, collection of articles, etc.) that walk thorugh example of ARIMA modeling with the ts package of R? Genarlly if you've just started to learn the R-language and are all week long with ARIMA, what is a fast-pace source that you would recommend (to a beginner) ? Thank you, Krasi

Windows 98 R : Copyright 2001, The R Development Core Team Version 1.2.1 (2001-01-15) Dear friends. How comes this works and produce a single prediction: x <- rnorm(15) y <- x + rnorm(15) predict(lm(y ~ x)) new <- data.frame(x = seq(-3, 3, 0.5)) predict(lm(y ~ x), new, se.fit = TRUE) pred.w.plim <- predict(lm(y ~ x), new, interval="confidence") new1 <- data.frame(x=3)

Dear R-devel, There seems to be a discrepancy in the order in which lm and rlm evaluate their arguments. This causes rlm to sometimes produce an error where lm is just fine. Here is a little script that illustrate the issue: > library(MASS) > ## create data > n <- 100 > dat <- data.frame(x=rep(c(-1,0,1), n), y=rnorm(3*n)) > > ## call lm, works fine > summary(lm(y ~

Dear Group, I am having trouble with using rlm on multivariate data sets. When I call rlm I get Error in lm.wfit(x, y, w, method = "qr") : incompatible dimensions lm on the same data sets seem to work well (see code example). Am I doing something wrong? I have already browsed through the forums and google but could not find any related discussions. I use Windows XP and R

Sorry to ask this but does anyone knows a good text that helps with the basic programming side of R. I understand the statistics behind most of R but i have real difficulty with the data types and how R views them. As a part of that could you help me with another problem. I know how to read data in from a file in terms of read.table but how do i single a column out or even just download a

I am looking at MM-estimates for some interlab comparison work. The usual situation in this particular context is a modest number of results from very expensive methods with abnormally well-characterised performance, so for once we have good "variance" estimates (which can differ substantially for good reason) from most labs. But there remains room for human error or unexpected chemistry

Greetings: I am trying to build R-2.0.1 from source on windows. My path is set to: .;C:\RStools;C:\MinGW\bin;C:\perl\bin;C:\texmf\miktex\bin;C:\HTMLws\;C:\R201\R201\bin;%System Root%\system32;%SystemRoot%;%SystemRoot%\System32\Wbem;C:\Program Files\ Common Files\Adaptec Shared\System;C:\LINGO9\ and Mkrules has been edited and reads # path (possibly full path) to same version of R on the host

Dear R list, What is the proper way to specify a nested model so that the F values agree with the expected mean square errors? Specifically, suppose I have a design where "Heads" are nested within "Machines". I would like to model the following Y_ijk = Mu + Machine_i +Head_j(i) +Error_k(ij). Using the commands below, > summary(aov(Strain~Machine + Head%in%Machine ))

Hi all, I am reading the MASS book but it doesn't give examples about the diagnosis and model checking for rlm... My data is highly non-Gaussian so I am using rlm instead of lm. My questions are: 0. Are goodness-of-fit and model-checking using rlm completely the same as usual regression? 1. Please give me some pointers about how to do goodness-of-fit and residual diagnosis for

Dear List, I used rlm to calculate two regression models for two data sets (rlm due to two outlying values in one of the data sets). Now I want to compare the two regression slopes. I came across some R-code of Spencer Graves in reply to a similar problem: http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg06666.html The code was: > df1 <- data.frame(x=1:10, y=1:10+rnorm(10))

Hello, I have a simple xyplot with rlm lines. I would like to add the a and b coefficients (y=ax+b) of the rlm calculation in each panel. I know I can do it 'outside' the xyplot command but I would like to do all at the same time. I found some posts with the same question, but no answer. Is it impossible ? Thanks in advance for your help. Ptit Bleu. x11(15,12) xyplot(df1$col2 ~