similar to: Optimal platform for R

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the

https://bugzilla.samba.org/show_bug.cgi?id=7889 Summary: Add "--backup-deleted" Product: rsync Version: 3.0.7 Platform: All OS/Version: All Status: NEW Severity: enhancement Priority: P3 Component: core AssignedTo: wayned at samba.org ReportedBy: jik at kamens.brookline.ma.us

Record plot which stores a plot to an internal R data structure and Replay Plot which replays the plot enables one to keep plots around and use them accross sessions at least until version 3. If you cannot restore old plots and treat them as data and save accross sessions, there is little reason for recordPlot. Either R needs to dispense with internal formats entirelly, a bad move in my opinion

If anyone has a list of application areas where there is extensive use of R, I'd like to hear of it. My current short list is: Bioinformatics Epidemiology Geophysics Agriculture and crop science John Maindonald Mathematical Sciences Institute, Australian National University. john.maindonald at anu.edu.au

Can anyone suggest an equivalent, for lattice graphs, of the base graphics argument bty="l"? NB that I am leaving off the box around the strip, with a strip function: stripfun <- function(which.given,which.panel, factor.levels=as.expression(levlist), ...){ panel.text(x=0, y=0.5, lab = as.expression(levlist[which.panel[which.given]]),

Does anyone know any more than is in the following press release about REvolution Computing and their commercialization of R? http://www.intel.com/capital/news/releases/080122.htm "Intel Capital, the global investment arm of Intel Corporation, today announced that it has invested in the Series A financing of REvolution Computing, creator of parallel computing software for computational

I propose the following enhancements and changes to plot.lm(), the most important of which is the addition of a Residuals vs Leverage plot. (1) A residual versus leverage plot has been added, available by specifying which = 5, and not included as one of the default plots. Contours of Cook's distance are included, by default at values of 0.5 and 1.0. The labeled points, if any, are those

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

Hi, I have been trying to add labels to the rows of a heatmap produced using image() function. It is simply not working. Here is what I did. A2Rplot.hclust(hcc,k=length(num),col.up="black",col.down=num,lty.up=2,lty.down=1,lwd.up=1,lwd.down=2,show.labels=FALSE) #used the above external program to create a colored dendrogram xsort <- x[1:nrow(x), hcc$labels[hcc$order]]

help(letters, htmlhelp=TRUE) fails. Under the Mac OSX gui, the message is 'Help for the topic "a" was not found.' Under the version documented below, and under Windows, the message is "No documentation for 'a' in specified packages and libraries:" repeated for all the elements of letters, then followed by "you could try

I sent the message below to this list over a week ago, and I haven''t seen any response. If this is not the correct forum for my question, can anyone suggest a better person or place to which I should direct it? Thank you, Jonathan Kamens ------- Start of forwarded message ------- From: Jonathan Kamens <jik@kamens.brookline.ma.us> To: lartc@mailman.ds9a.nl Subject: [LARTC] MSS

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

Consider the following analysis of a class experiment done as a Latin Square: > spinner <- gl(4,4,16,label=c("Murray","Angela","Shasha","Stephen")) > order <- gl(4,1,16) > treat <- scan() 1: 1 2 4 3 5: 4 3 1 2 9: 3 4 2 1 13: 2 1 3 4 17: Read 16 items > coin <-

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,

I apologize if this question is not completely appropriate for this list. I have been using SAS for a while and am now in the process of learning some C and R as a part of my graduate studies. All of the statistical packages I have used generally yield p-values as a default output to standard procedures. This week I have been reading "Testing Precise Hypotheses" by J.O. Berger

You need to give the model formula that gave your output. There are two sources of variation (at least), within and between locations; though it looks as though your analysis may have tried to account for this (but if so, the terms are not laid out in a way that makes for ready interpretation. The design is such (two locations) that you do not have much of a check that effects are consistent over

Dear R community, In the excellent nlme package the default diagnostic plot graphs the innermost residuals against innermost fitted values. I recently fit a mixed-effects model in which there was a very clear positive linear trend in this plot. I inferred that this trend occurred because my fixed effect was a two-level factor, and my random effect was a 12-level factor. The negative residuals

Dear R community, I am trying to resolve a discrepancy between the way SPSS and R handle 2-way, repeated measures ANOVA. An experiment was performed in which samples were drawn before and after treatment of four groups of subjects (control and disease states 1, 2 and 3). Each group contained five subjects. An experimental measurement was performed on each sample to yield a

G''day I''ve been performing an analysis of some data by a follow the leader method (Bad I know but it seems to work and I think I understand all the steps now) and I''ve run into a problem. The analysis is for all practical purposes identical to the non-linear mixed effects analysis performed on Ludbrooks Rabbit data in the second edition of Venables & Ripley (page