similar to: predict.glm

Full_Name: Rob Foxall Version: 2.2.0 OS: Windows XP Submission from: (NULL) (149.155.96.5) I was using multinom from nnet package, when I did something stupid -- I entered in an incorrect factor variable as response. This factor had only one level. Instead of R telling me not to be so dumb, it crashed, clicking on debug coming up with the message "An exception 'Unhandled Win32

Hello all, A colleague at work set me the challenge to convert some MATLAB code into R, to see which is faster. We'd seen that benchmark comparing MATLAB 6.5 to R1.90 (and others), and so I thought that I should be able to get roughly comparable speeds. The code has lots of multiplications of matrixes, transposes, and MATLAB's "repmat". I did the code conversion, and R was about

Hi all, Easy question for you (which I failed to find the answer to in the FAQ etc). I've recently been forced to switch from linux to windows (currently windows NT), and my usual habit of putting lots of "cat" statements in slow functions to get an idea of the progress rate is no longer useful. Why -- because R waits until the function is completely finished before printing the cat

Dear List, This is just a programming problem which i cannot seem to figure out. I am trying to get a set of power from a test (say, kolmogorov smirnov) out of a distribution (say, G-K distribution) as follows. I am trying to reduce to pain of writing the whole set of data points (p# below) using "for" loop. However, I seem to have some problem in it as the output "M" does not

Dear R-Help, I have written a function, which in simplified format can be represented as: temp.fn <- function(my.mean,my.sd){ Parameters <- list(mean = my.mean, sd = my.sd) curve(do.call(dnorm,c(list(x), Parameters)), from = my.mean-my.sd, to = my.mean+my.sd) } This works as I want it do. Wishing to immortalise this function into my very own package however, results in the following

Hello all, A really noddy question for you all: I''m trying without success to do some subhypothesis testing. Using simple anova model, with a toy dataset from a book. I have four factors A,B,C,D, and wish to test mu_C = mu_D. This is what I have tried: > contrasts(infants$group,how.many=1) <- c(0,0,1,-1) > contrasts(infants$group) [,1] A 0 B 0 C 1

Hello, I have an expression for the y axis, which I wish to rotate by 90 degrees (so that this label has the same orientation as for the x axis). Thought the graphical parameter crt (or srt) would do the trick, but haven't had any joy so far. Any ideas? (Using Version 1.2.2, linux, redhat 7.0) Cheers, Rob. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Dear R-helpers, I have a model created by lda, and I would like to use this model to make predictions for new or old data. The catch is, I want to do this without using the "predict" function, i.e. only using information directly from the foo.lda object to create my posterior probabilities. In anticipation of likely responses, I will be brushing up my lda knowledge using the

Hi, I have many printers installed under cups on a samba server, so the printers can be used and installed on Mac and PCs. My problem appeared with some new printers I bought and their PPDs. Under Cups, all the PPD options (Page Sizes, Trays, etc..) appear if I looked at them from the cups printer configuration page or if I use it from a Mac. But if I install the printers under windows

Dear R-help, I have a character vector, some elements will be numeric, some not, and some even empty. E.g.: temp1 <- c("abcd"," 2 ","") I'm only interested in the numeric elements, the rest I can just throw away. It is easy enough to loop through the vector: temp <- try(eval(parse(text=temp1[1])), silent=TRUE); class(temp) # try-error temp <-

I am including my config.log in the hopes that someone can tell me what is going on with dovecot installing on Mac OSX 10.2.6 Sincerely, ? Roger Cates, CCNA Vice President & Chief Technical Officer Xpower Internet, LLC Xpowerhosting.com | Xpoweronline.com P 888.245.7501 | F 270.338.4602 Internet to the power of X. ? begin 666 dovecot_config.log

Hi. This is the qmail-send program at provida.org.br. I'm afraid I wasn't able to deliver your message to the following addresses. This is a permanent error; I've given up. Sorry it didn't work out. <musical@provida.org.br>: vdeliver: ERRO: Usu?rio n?o existe: 'musical'. --- Below this line is a copy of the message. Return-Path: <r-bugs@r-project.org>

Hi Vishal, Ruijie, Thanks for your interest in the project. To get started, the first task would be to merge the 5 patches on top of trunk llvm. The list of patches are listed in the project description: http://llvm.org/OpenProjects.html#llvm_mergesim Please create an account in llvm phabricator (reviews.llvm.org) if you haven't already, and put your patches there. Let me know if you have

Hello, I'm Ruijie Fang, currently a 1st-year undergraduate at Princeton University, majoring in Computer Science. I am writing to to express my interest in the Google Summer of Code project: Improve MergeFunctions to incorporate MergeSimilarFunction patches and ThinLTO Support. I've read the LCTES'14 paper and found it quite interesting and comprehensible. I've also went as far

Hi, I''ve got a newbie question--sorry if this is covered elsewhere, I parsed through the archives for awhile and didn''t see it. I''d like to listen for whenever a file is renamed (e.g. foo.txt -> foo.old) and then magically change it back. This sounds odd, but I''m working with a stubborn application and this will actually make things work nice. So, if I do:

On Tue, Aug 4, 2020 at 10:51 PM aditya kumar <hiraditya at gmail.com> wrote: > Glad to hear that there is an interest in a function splitting pass. There > are advantages to splitting functions at different stages as you've already > noted. > Right -- with slightly different objectives. Machine Function Splitting Pass's main focus is on performance improvement. > -

Greetings, We present “Machine Function Splitter”, a codegen optimization pass which splits functions into hot and cold parts. This pass leverages the basic block sections feature recently introduced in LLVM from the Propeller project. The pass targets functions with profile coverage, identifies cold blocks and moves them to a separate section. The linker groups all cold blocks across functions