similar to: Using R for Data Analysis & Graphics: update

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> De : Bill Venables <venables at acland.qld.cmis.csiro.au> > Objet : Re: [R] paste ? > Date?: mardi 25 avril 2000 08:45 > (...) > Secondly, I'm curious about the history of this kind of plot. > I've only heard it called a "Tukey mean difference" plot, (and > Trellis graphics has a function, tmd(), that does it, but no one > knows about it...).

Dear friends. I've made a very simple procedure to make Bland-Altman plots and it works OK except for the inability to take a variate as argument in annotation. I'm sure it is a very simple error, so if you have 5 seconds please tell me. Best wishes Troels bland <- function (x) #accepts two columns { mn <- 0.5*(x[,1]+x[,2]) diff <- x[,1]-x[,2]

OK, I see now that I was supposed to twig that the reference was to putting the ?.Rnw' files back into the vignettes directory from the inst/doc directory where they?d been placed in the course of creating the tar.gz file. I am still trying to work out what I need to put into ?.Rinstignore? so that ?.install_extras? is not installed. John Maindonald email: john.maindonald at

Sorry. This, and the description in the ?Writing R Extensions? manual, leaves me completely mystified. Is it that I have to remove the PDFs that are created when I run ?R CMD build?, and somehow ensure that they are rebuilt when the package is installed? Do I need a Makefile? John Maindonald email: john.maindonald at anu.edu.au<mailto:john.maindonald at anu.edu.au> phone :

You've omitted a comma. races2000 is a data frame, which for purposes of extracting rows behaves like a 2-dimenional object. The following works fine: hills2000 <- races2000[races2000$type == 'hill', ] Additionally, you might like to ponder > type <- races2000[names(races2000)=="type"] > type[1:4] Error in "[.data.frame"(type, 1:4) :

Dear R people Could you please help with following Trying to compare accuracy of tumor size evaluation by different methods. So data looks like id true metod1 method2 ... 1 2 2 2.5 2 1.5 2 2 3 2 2 2 etc. Could you please give a hint how to deal with that. Seems like {merror} does not suite to me because I am trying to compare accuracy of measurements with their true known values not just

I am looking for information on experimentation with the use of R in the teaching of statistics and science in schools. Any leads would be very welcome. I am certain that there is such experimentation. I've made this inquiry on r-sig-teaching, with no response. John. John Maindonald email: john.maindonald at anu.edu.au phone : +61 2 (6125)3473 fax : +61 2(6125)5549 Centre

In Section 1.4.2 of "Writing R Extensions" %\VignetteEngine{knitr::knitr} should be %\VignetteEngine{knitr::knit} > sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-apple-darwin10.8.0 (64-bit) Is this sort of thing best reported here, or is a huge report in order? John Maindonald email: john.maindonald at anu.edu.au phone : +61 2 (6125)3473 fax

<<< Thus factor analysis is in essence a model for the correlation matrix of x, ? = ?'? + ? >>> This should surely be ? = ??' + ? Also line 3 under ?Details? says <<< for a p?element row-vector x, ? >>> x is here surely a column vector, albeit the transpose of a row vector from the data matrix. cf page 322 of ?Modern Applied Statistics with S?, 4th

The following is 'semicolon.Rnw' > \SweaveOpts{engine=R, keep.source=TRUE} > > <<xycig-A, eval=f, echo=f>>= > library(SMIR); data(bronchit); library(KernSmooth) > @ % > > Code for panel A is > <<code-xycig-A, eval=f, echo=t>>= > <<xycig-A>> > @ % Sweave("semicolon") yields the following 'semicolon.tex'

I have posted, at http://wwwmaths.anu.edu.au/~john/r/newsets/ image (.rda) files, and first stabs at .Rd files for various data on deaths in London from 1629 to 1939. (There are of course gaps.) The sources (Guy 1882 & Stocks 1942) are documented in the .Rd files: (1) poxetc: measles, smallpox & total deaths: 1629-1881 [I have deliberately left several inconsistencies that were in

expression() accepts multiple expressions as arguments, thus: plot(1:2, 1:2) legend("topleft", expression(y == a * x^b, "where "* paste(y=="wood; ", x=="dbh"))) Is there a way to do this when values are to be substituted for a and b? i.e., the first element of the legend argument to legend() becomes,

rug() may add integer axis labels when called with a POSIXlt object as argument. dtimes <- c("09/29/2007 12:54", "09/30/2007 00:14", "10/01/2007 00:14", "10/02/2007 00:14", "10/03/2007 00:14", "10/04/2007 00:14", "10/05/2007 00:14", "10/06/2007 00:14", "10/07/2007

# r-bugs@r-project.org The problem occurs when the model matrix has a single column. > elastic <- data.frame(stretch=c(46,54,48,50,44,42,52), distance=c(183,217,189,208,178,150,249)) > elastic.lm <- lm(distance ~ -1 + stretch, data=elastic) > predict(elastic.lm,se=T) Error in XRinv^2 %*% rep(res.var, p) : non-conformable arguments The fix is to replace XRinv <-

Hi Ross - I believe I was wrong in thinking that passing via the ... list to stripchart() was ever allowed. Here are patches: Add ... to the argument list Add, at the beginning of the function: pars <- list(...) There are two calls to axis(). Modify these to: axis(1, at = at, labels = names(groups), las=pars$las) axis(2, at = at, labels = names(groups), las=pars$las) Also

Finding and not unnecessarily duplicating existing functionality is important also from a user perspective. Negative binomial regression provides a somewhat extreme example of existing overlap between packages, with the scope that this creates for confusing users, especially as the notation is not consistent between these different implementations. In addition to MASS::glm.nb(), note

With lme4, use of mcmcsamp can be insightful. (Douglas Bates drew my attention to this function in a private exchange of emails.) The distributions of random effects are simulated on a log scale, where the distributions are much closer to symmetry than on the scale of the random effects themselves. As far as I can see, this is a straightforward use of MCMC to estimate model parameters; it is not

I'd use mcmcsamp() to examine the posterior distribution, under a relatively uninformative prior, of of the parameter estimates. For estimates that are based on four or five or more "degrees of freedom", I'd surmise that the prior will not matter too much. With estimates where the number of "degrees of freedom" is one or two or three, the posterior distribution may vary

The inst/doc directory of the DAAG package has 6 files coral551.spot, ... that are around 0.85 MB each. It would be useful to be able to zip then, but that as matters stand interferes with the use of the Sweave file that uses them to demonstrate input of expression array data that is in the "spot" format. They do not automatically get unzipped when required. I have checked that