similar to: table problems

Dear all, How does the effect() function in the effects package calculate effects and standard errors for glm quasipoisson models? I was using effect() to calculate the impact of increasing x to e + epsilon, and then finding the expected percent change. I thought that this effect (as a percentage) should be exp(beta*epsilon), where beta is the appropriate coefficient from the model, but

Hi, Please consider the following : x = c(1,3,NA,5) y = c(2,NA,4,1) min(x,y,na.rm=TRUE) # ok [1] 1 max(x,y,na.rm=TRUE) # ok [1] 5 sum(x,y,na.rm=TRUE) # ok [1] 16 pmin(x,y,na.rm=TRUE) # ok [1] 1 3 4 1 pmax(x,y,na.rm=TRUE) # ok [1] 2 3 4 5 psum(x,y,na.rm=TRUE) [1] 3 3 4 6 # expected result Error: could not find function "psum" # actual result

Hi list! I have a large matrix which I'd like to partition into blocks and for each block I'd like to compute the mean. Following a example where each letter marks a block of the partition: a a a d g g a a a d g g a a a d g g b b b e h h b b b e h h c c c f i i I'm only interested in the resulting matrix of means. How can this be done efficiently?

In Mathematica there is a neat feature, where you can change the head of a list from "list" to say "+" and obtain a sum of the list elements. I can't find a way to sum a list of vectors of same length or list of matrices of the same dimension and was curious if something like that exists in R. do.call("+",list) doesn't work because "+" accepts only

Can any one help me to solve problem in my code? I am actually trying to find the stopping index N. So first I generate random numbers from normals. There is no problem in finding the first stopping index. Now I want to find the second stopping index using obeservation starting from the one after the first stopping index. E.g. If my first stopping index was 5. I want to set 6th observation from

I''m developing a system similar to the ForumExample (http:// wiki.rubyonrails.org/rails/pages/ForumExample), and stumbled upon the following. Let''s say I wanted to get all the categories a certain user posted in. How do I get that information withouth using a custom query through :finder_sql (and thus losing the find_in_collection ownage)? It would also be great to see how many

Version 3.9 of the survey package is now on CRAN. Since the last announcement (version 3.6-11, about a year ago) the main changes are - Database-backed survey objects: the data can live in a SQLite (or other DBI-compatible) database and be loaded as needed. - Ordinal logistic regression - Support for the 'mitools' package and multiply-imputed data - Conditioning plots,

Version 3.9 of the survey package is now on CRAN. Since the last announcement (version 3.6-11, about a year ago) the main changes are - Database-backed survey objects: the data can live in a SQLite (or other DBI-compatible) database and be loaded as needed. - Ordinal logistic regression - Support for the 'mitools' package and multiply-imputed data - Conditioning plots,

Dear R-ophiles, I've found something very odd when I apply convolve to ever larger vectors. Here is an example below with vectors ranging from 2^11 to 2^17. There is a funny bump up at 2^12. Then it gets very slow at 2^16. > for( i in 11:20 )print( system.time(convolve(1:2^i,1:2^i,type="o"))) user system elapsed 0.002 0.000 0.002 user system elapsed 0.373

I want to do something like this in R. If I have three vectors > a1 [1] 1 2 3 > a2 [1] 4 5 6 > a3 [1] 9 10 7 I want to compute 1. A vector that is the mean at each serial position of a1, a2, and a3. so in this example it would have the contents 4.667, 5.667, 5.333333 2. A vector that is the SD at each serial position of a1, a2, and a3. so in this example it would have the contents

On Wed, Oct 14, 2015 at 2:02 AM Bill Kelly via llvm-dev < llvm-dev at lists.llvm.org> wrote: > Tanya Lattner wrote: > >> On Oct 13, 2015, at 10:23 AM, Bill Kelly via llvm-dev < > llvm-dev at lists.llvm.org> wrote: > >> > >> Renato Golin via llvm-dev wrote: > >>> On 13 October 2015 at 17:16, Kuperstein, Michael M via llvm-dev >

Hello experts I have a thing which I can't vectorize. Can any gurus out there help? This is my truncated dataset; it is a dataframe of five possums and which tree they slept in on three days. R> dataset p14 p88 p82 p90 p98 1 6 7 5 1 3 2 8 9 10 1 11 3 8 7 12 13 14 I can manipulate it with simple functions fine: R> f function(n){n+1000} R> f(dataset)

http://dovecot.org/releases/1.1/dovecot-1.1.0.tar.gz http://dovecot.org/releases/1.1/dovecot-1.1.0.tar.gz.sig Two hours later than promised, I slept longer than intended. :) No changes since v1.1.rc13. Below are the largest changes since v1.0: * After Dovecot v1.1 has modified index or dovecot-uidlist files, they can't be opened anymore with Dovecot versions earlier than v1.0.2. *

http://dovecot.org/releases/1.1/dovecot-1.1.0.tar.gz http://dovecot.org/releases/1.1/dovecot-1.1.0.tar.gz.sig Two hours later than promised, I slept longer than intended. :) No changes since v1.1.rc13. Below are the largest changes since v1.0: * After Dovecot v1.1 has modified index or dovecot-uidlist files, they can't be opened anymore with Dovecot versions earlier than v1.0.2. *

I'm a student currently working with the *sleepstudy* dataset in matrix.pkg. It deals with the reaction times of sleep deprived students over a period of days. I am trying to model reaction times in order to describe the variation between students by days they havent slept. This is what I'm running in R, but unfortunately I'm missing something: > logmod11 <-

Hey all, I'm coloring outside the lines a little bit here but I would like to automate the install of a samba pdc. Within that script to install I would like to assign rights to a group. Here is an example of a few steps: # Create Unix group: groupadd domadmins # Map unix group to samba groups: net groupmap add ntgroup="Domain Admins" unixgroup=domadmins rid=512 type=d # Assign

On Thu, Sep 18, 2014 at 6:28 PM, Andy Lutomirski <luto at amacapital.net> wrote: > On Thu, Sep 18, 2014 at 6:03 PM, Andy Lutomirski <luto at amacapital.net> wrote: >> On Thu, Sep 18, 2014 at 5:49 PM, Nakajima, Jun <jun.nakajima at intel.com> wrote: >>> On Thu, Sep 18, 2014 at 3:07 PM, Andy Lutomirski <luto at amacapital.net> wrote: >>>

On Thu, Sep 18, 2014 at 6:28 PM, Andy Lutomirski <luto at amacapital.net> wrote: > On Thu, Sep 18, 2014 at 6:03 PM, Andy Lutomirski <luto at amacapital.net> wrote: >> On Thu, Sep 18, 2014 at 5:49 PM, Nakajima, Jun <jun.nakajima at intel.com> wrote: >>> On Thu, Sep 18, 2014 at 3:07 PM, Andy Lutomirski <luto at amacapital.net> wrote: >>>

Dear helplist Many many thanks to everyone who helped me. The trick was to use tabulate() or, better, tab <- rep(0,50) names(tab) <- 1:50 tab[names(table(sleeps))] <- table(c) My original dataset was a list of 50 trees and a length 12 vector recording which tree a certain possum slept in on 12 nights. As Professor Ripley points out, a Monte-Carlo simulation is easy to set up, and it

I'm looking for advice or suggestions for rolling log files with a daemon. I have a python script that I daemonized with http://www.jejik.com/articles/2007/02/a_simple_unix_linux_daemon_in_python/. Before I daemonized it it was run from a bash script that invoked the underlying python script. It ran the python script, waited for it to complete and then it slept for 5 seconds and ran it again.