similar to: Inconsistency, possibly a bug? (PR#758)

Just a few documentation "bugs" that I've noticed recently. 1. In the help for (dpqr)weibull(), the formula given for the variance of a Weibull is wrong. The correct formula is b^2 * sqrt(gamma(1 + 2/a) - (gamma(1 + 1/a))^2)) Note that I've also changed Gamma to gamma, which I think is preferable since this is actually the name of the gamma() function

Here's a simple example: > x <- rnorm(20) > y <- 1 + 10*x + rnorm(20) > plot(y ~ x, subset=2*(1:10),xlab="In",ylab="Out") Error in length(x) == l : comparison (1) is possible only for vector types The problem seems to be in this section of the code for plot.formula: if (!missing(subset)) { s <- eval(m$subset, data, parent.frame()) l

{why do I get more and more messages sent to "owner-r-devel" or "owner-r-help" ?? "owner-foo" by convention (even RFC ???) is the (set of) maintainer(s) of mailing list "foo".. Please *do* post to "R-help" , "R-devel", and *NOT* to "owner-...." ! Martin Maechler = current owner-r-... ------- start of forwarded

Over a decade ago there was a problem with model.frame when the variable names were long: https://stat.ethz.ch/pipermail/r-help/2002-August/024492.html I have similar symptoms with R 2.15.3 on Windows 7: Browse[2]> x <- model.matrix(formula(myform), p$data) Error in model.frame.default(object, data, xlev = xlev) (from mice.R#601) : object is not a matrix My attempt at a work-around

Hi Peter and others, If it helps, I wrote a small function glm.scoretest() for the statmod package on CRAN to compute score tests from glm fits. The score test for adding a covariate, or any set of covariates, can be extracted very neatly from the standard glm output, although you probably already know that. Regards Gordon --------------------------------------------- Professor Gordon K

# r-bugs@r-project.org `predict' complains about new factor levels, even if the "new" levels are merely levels in the original that didn't occur in the original fit and were sensibly dropped, and that don't occur in the prediction data either. (At least if `drop.unused.levels' was set to TRUE, which the default.) test> scrunge.data.2_ data.frame( y=runif( 3),

{again a message that was sent to owner-r-help (which is me, currently) why on earth ???!??!? reply to R-help or the original sender Brett Presnell; } I'm teaching a categorical data analysis course this term, and a minor "problem" has resurfaced that I have often thought about before. This applies equally to Splus I suppose, but my undergrads aren't using Splus. It

Dear All, We are about to purchase the second edition of Agresti's "Categorical Data Analysis" (my old copy of the first ed. of that wonderful book is falling apart). I would appreciate suggestions about other comparable books which, if possible, have examples using R/S code (instead of SAS). Thanks, Ram?n -- Ram?n D?az-Uriarte Bioinformatics Unit Centro Nacional de

Dear DevelopeRs, in continuing with my suite of packages on experimental design, I am stuck with an issue that appears to be related to package AlgDesign - I have tried to get it solved by Bob Wheeler, but he seems to be stuck as well. Whenever AlgDesign is loaded, some of my code does not work any more. For example, in a fresh R session: require(DoE.base) fac.design(nlevels=c(2,6,2))

Hi, I'm trying to get sparse.model.matrix to retain unused levels. I can't seem to get this working through the most obvious routes such as specifying drop.unused.levels = FALSE in the model.frame or trying to pass all levels in xlev,which is an argument to sparse.model.matrix (see code below). Any help would be gratefully received. Cheers, Jarrod fac<-factor(rep(1:10,10),

I'm working on predict.survreg and am confused about xlevels. The model.frame method has the argument, but none of the standard methods (model.frame.lm, model.frame.glm) appear to make use of it. The documentation for model.matrix states: xlev: to be used as argument of model.frame if data has no "terms" attribute. But the terms attribute has no xlevels information in it, so I

Full_Name: Sharon Kuhlmann Version: 0.99 OS: solaris2.6 Submission from: (NULL) (129.16.167.231) When I use the function > eigen(x), it gives me a "Process R bus error", and the program quits. I'm running the following R version: platform sparc-sun-solaris2.6 arch sparc os solaris2.6 system sparc, solaris2.6 status Patched

Is there any reason that rstandard.glm doesn't have a "pearson" option? And if not, can it be added? Background: I'm currently teaching an undergrad/grad-service course from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" and deviance residuals are not used in the text. For now I'll just provide the students with a simple function to use, but I

I think there are two bugs in aov() that shows up when the right hand side of `formula' contains both `-1' and an Error() term, e.g., aov(y ~ a + b - 1 + Error(c), ...). Without `-1' or `Error()' there is no problem. I've included and example, and the source of aov() with suggested fixes below. The first bug (labeled BUG 1 below) creates an extra, empty stratum inside

Full_Name: Glenn Stone Version: 1.5.1 and 1.6.0 OS: win2000 Submission from: (NULL) (168.140.227.9) The following code produces incorrect fitted values in version 1.5.1 and an error in 1.6.0 Error in "contrasts<-"(*tmp*, value = "contr.treatment") : contrasts apply only to factors In addition: Warning message: variable ihalf is not a factor in:

I believe you are right, but can you please explain why anyone would want to fit this model? It differs only in the coding from aov(y ~ a + b + Error(c), data=test.df) and merely lumps together the top two strata. There is a much simpler fix: in the line if(intercept) nmstrata <- c("(Intercept)", nmstrata) remove the condition (and drop the empty stratum later if you

I'm posting this for two reasons: one is to see if anyone has a better way of solving the problem or suggestions for improving my existing approach; and the other is to show what I'm currently doing in case anyone else might find it useful. The background is that I've been using Sweave for several years now to produce class notes, and I sometimes include quite a lot of raw R input and

I'll start with my apologies to Martin for sending those last two of message to the list owner (the last one he forwarded was actually sent a week or two ago and must have been lost in the ether somehow). This is basically due to fast copy/paste in emacs without paying much attention to what I'm doing. Lack of sleep is my excuse, but I don't know if that goes very far with the

Hi all, I''m still not quite happy with the NA and factor handling of lm and predict.lm in R1.6.1 (forcing me to use my not very skillfully crafted patches). Here is the problem 1: >

I'm trying to use the vcd package to produce mosaic plots for my class notes, written in Sweave and using the LaTeX's beamer document class. For projecting the notes in class, I use a dark background with light foreground colors. It's easy enough to change the defaults for R's standard graphics to match my color scheme (using the fg, col.axis, col.lab, col.main, and col.sub