similar to: predict (PR#2686)

There is a bug in `predict' whereby the order of variables sometimes gets re-arranged compared to the original fit, and then disaster results. Specifically, the 'variables' and 'predvars' attributes of a 'terms' object get out of synch. This only happens when the terms in the original formula get re-ordered during fitting: test> scrunge.data_ data.frame(

I'm encountering problems when making lazy-loadable databases of the output from 'parse_Rd'. The lazy-load database is of seemingly limitless size when I try to reload it... Admittedly I am using functions that I'm not really supposed to use, which is why this isn't a bug report, but there does seem to be something strange going on; my code is very similar to code that lives

Happy New Year I recognize this is a low priority issue, but... I'll fix it if you let me. There are some TABs where R style calls for 4 spaces. For example R-3.0.2/src/library/stats/R/dummy.coef.R. I never noticed this until today, when I was stranded on a deserted island with only the R source code and a Swiss Army knife (vi). Now I realize my ~.vimrc has tabstop set at 2, and it makes

Hello R devel/help, I ran into this strange behavior: # showstack is supposed to walk through the stack of parent # environments when it is called: showstack = function() { env = environment() for(i in 1:12) { env = do.call(parent.frame, list(), env=env) print(env) } } # a simple chain of functions: g3=function(t) showstack()

Hello R devel/help, I ran into this strange behavior: # showstack is supposed to walk through the stack of parent # environments when it is called: showstack = function() { env = environment() for(i in 1:12) { env = do.call(parent.frame, list(), env=env) print(env) } } # a simple chain of functions: g3=function(t) showstack()

Dear R-devel When 'install.packages' runs, it updates all html files in all packages. Mostly, there seems to be no actual change to the html file contents, but the date/time does change. This has causing been me a bit of trouble, because I keep synchronized versions of R on several different machines, and whenever I install a package, many MB of file transfers are required; my slow upload

Full_Name: Mark Bravington Version: 2.0.1 OS: Windows XP Submission from: (NULL) (140.79.22.104) 'getAnywhere' crashes when its argument starts with a period: > getAnywhere( '.onLoad') Error in exists(x, envir, mode, inherits) : invalid first argument One fix might be to replace the line if ( !is.null(f <- getS3method(gen, cl, TRUE))) { with if ( nchar( gen)

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

# # r-bugs@r-project.org # ###################################################### Because I work in different directories, but always want to save my .Rhistory in the same place, I have changed the system function savehistory to this: function (file = "D:/R50/.Rhistory") invisible(.Internal(savehistory(file))) When I use q() to quit R, and it asks me whether I want to

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. Send mail to mime@docserver.cac.washington.edu for more info. --1133331701-635408861-993124011=:10449 Content-Type: TEXT/PLAIN; charset=US-ASCII I'm not sure if the warning messages Warning messages: 1: variable 67 is not a factor in:

After a bunch of discussion in the core group, we have decided to deprecate the delay() function (which was introduced as "experimental" in R 0.50). This is the function that duplicates in R code the delayed evaluation mechanism (the promise) that's used in evaluating function arguments. The problem with delay() was that it was handled inconsistently (e.g. sometimes you would see

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))

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

I would like to be able to trace execution into calls below the current function, or to follow execution as calls return. This is roughly the distinction between "step" and "next" in many debuggers. I would also like to be able to switch to a location further up the call stack than the location at which I enter the debugger, to see the context of the current operations. Are

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:

Seems to be a day for finding peculiar little things. There is an inconsistency in the behavior of lm vis a vis glm: > x <- rnorm(15) > y <- 1 + 10*x + rnorm(15) > z <- as.factor(rep(c("A","B","C"),rep(5,3))) > xyz <- data.frame(x,y,z) > fit.lm <- lm(y ~ x + z, data=xyz, subset=(z != "C")) > fit.glm <- glm(y ~ x +

Hello, I have tried reading the documentation and googling for the answer but reviewing the online matches I end up more confused than before. My problem is apparently simple. I fit a glm model (2^k experiment), and then I would like to predict the response variable (Throughput) for unseen factor levels. When I try to predict I get the following error: > throughput.pred <-

I am trying to replicate the first example from stepAIC from the MASS package with my own dataset but am running into error. If someone can point where I have gone wrong, I would appreciate it very much. Here is an example : set.seed(1) df <- data.frame( x1=rnorm(1000), x2=rnorm(1000), x3=rnorm(1000) ) df$y <- 0.5*df$x1 + rnorm(1000, mean=8, sd=0.5) # pairs(df); head(df) lo <-

I need predict to ignore rows that contain levels not in the model. Consider a data frame, "const", that has columns for the number of days required to construct a site and the city and state the site was constructed in. g<-lm(days~city,data=const) Some of the sites in const have not yet been completed, and therefore they have days==NA. I want to predict how many days these sites

Hi, I have run into another problem using offsets, this time with the predict function, where there seems to be a contradiction again between the behavior and the help page. On the man page for predict.lm, it says Offsets specified by offset in the fit by lm will not be included in predictions, whereas those specified by an offset term in the formula will be. While it indicates nothings about