similar to: Wishlist: xtabs and table to optionally use attribute value labels (PR#8659)

Full_Name: Ulrike Gr?mping Version: 2.2.1 OS: Windows Submission from: (NULL) (84.190.139.94) When importing data from SPSS, it is a nice feature of the package foreign that it allows (option use.value.labels=F) to work with the original SPSS codes while keeping the value labels as information in an attribute. Unfortunately, after merging or subsetting, these attributes disappear. The code

Dear List, I have been trying to extract associations from a matrix whereby individual locations are within a certain distance threshold from one another. I have been able to extract those individuals where there is 'no interaction' (i.e. where these individuals are not within a specified distance threshold from another individual) and give these individuals a unique Group ID containing

Hi all, I am working with some social network analysis in R and ran into a problem I just cannot solve. Each observation in my data consists of a respondent, some characteristics and up to five friends. The problem is that all of these five friends might no show up later as a respondent (observation). Therefore I might not have characteristics on all the friends listed in the data and I want to

Hi there, Any update on this? Should I create bugzilla ticket and submit patch? Regards Jan Gorecki On 20 December 2016 at 01:27, Jan Gorecki <J.Gorecki at wit.edu.pl> wrote: > Hello, > > colnames seems to be not optimized well for data.frame. It escapes > processing for data.frame in > > if (is.data.frame(x) && do.NULL) > return(names(x)) > > but

Full_Name: Ulrike Gr?mping Version: 2.3.0 OS: Windows Submission from: (NULL) (84.190.150.205) Whenever any observations are excluded from a regression analysis (lm, glm, and other similar procedures) because of missing values, I would find it very useful if this fact is directly visible from the output. I think that the information should not only be available (I can e.g. look at length of the

Hello, colnames seems to be not optimized well for data.frame. It escapes processing for data.frame in if (is.data.frame(x) && do.NULL) return(names(x)) but only when do.NULL true. This makes huge difference when do.NULL false. Minimal edit to `colnames`: if (is.data.frame(x)) { nm <- names(x) if (do.NULL || !is.null(nm)) return(nm) else

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. --27464147-1097610713-1146500823=:15100 Content-Type: TEXT/PLAIN; CHARSET=ISO-8859-1; FORMAT=flowed Content-Transfer-Encoding: 8BIT Content-ID: <Pine.LNX.4.64.0605100926211.14434 at gannet.stats.ox.ac.uk> You are apparently unaware that

A problem has been pointed out by a French user of the survival package and I'm looking for a pointer. > options(OutDec= ",") > fit <- survfit(Surv(1:6 /2) ~ 1) > fit$time [1] NA 1 NA 2 NA 3 A year or two ago some test cases that broke survfit were presented to me. The heart of the problem was numbers that were almost identical, where table(x) and unique(x) gave

Use only plain text emails. Don't attach file types that will be stripped. See the footer at the bottom of your email for more information. Do give us the data using dput(): > dput(testbarplot) structure(list(V1 = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), V2 = c(3L, 8L, 1L, 9L, 5L, 4L, 2L, 0L), V3 = structure(c(1L, 2L, 3L, 4L, 3L, 5L, 6L, 7L), .Label = c("A", "B",

Hello, I would like to increase the number of iterations for running a Buckley-James regression model in the rms package, but something is apparently syntactically wrong. The following code initially is exactly as it appears in the help page (which runs successfully), then a "failure to converge" message (resulting from specifying an 'identity' link argument, the error message

Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10)

Dear R community   I have a large data set contain some empty cells. Because of that,  may be I am wrong, <NA> values are produced. Now I want replace both empty and <NA> values with zero.   Elder1 <- data.frame(   ID=c("ID1","ID2","ID3","ID6","ID8"),   age=c(38,35,"",NA,NA)) Output I am expecting   ID   age ID1  38 ID2  35

Hi, I would like to write a function which has the following syntax: myfn <- function(formula, ftime, fstatus, data) { # step 1: obtain terms in `formula' from dataframe `data' # step 2: obtain ftime from `data' # step 3: obtain fstatus from `data' # step 4: do model estimation # step 5: return results } The user would call this function as: myfn(formula=myform,

Uwe, I am talking binaries and Windows. I was not thinking of building binaries for old versions, but of looking in the new version repositories first, whether there is a binary that is said to be valid for the older version. Wouldn't it be possible to have the process look up a table of a newer R version for compatible binaries ? I got stuck with R 2-8.1 (which is not that old and should

Readers, The attempt is to create a bar plot with text labels adjacent to each datum value. Data file: 1,3,A 1,8,B 1,1,C 1,9,D 2,5,C 2,4,E 2,2,F 2,0,G testbarplot<-read.csv('data1.csv', header=FALSE) barplot(axes=FALSE, ann=FALSE, horiz=TRUE, testbarplot[,2], ylab= 'group', xlab= '(x values)', space=c(1,0,0,0, 1,0,0,0)) text(testbarplot[,2], testbarplot[,1],

I'm new to R (previously used SAS primarily) and I have a genetics data frame consisting of genotypes for each of 300+ subjects (ID1, ID2, ID3, ...) at 3000+ genetic locations (SNP1, SNP2, SNP3...). A small subset of the data is shown below: SNP_ID SNP1 SNP2 SNP3 SNP4 Maj_Allele C G C A Min_Allele T A T G ID1 CC GG CT AA ID2 CC GG CC AA ID3 CC GG nc AA

Ulrike, if you install from source, you always get the most recent version of the package given it does not depend on a newer version of R. If you want a binary package, you also get the newest version - that was newest at the time we stopped building binaries for that version of R. We (or better I if we only talk about Windows, but similar for all other platforms) cannot build for each R

Hello everyone, (This is my second question posted today on the R list). I am carrying out a competing risks analysis using the cuminc function...this takes the form: cuminc(ftime,fstatus,group) In my study, fstatus has 3 different causes of failure (1,2,3) there are also censored cases (0). "group" has two levels (0 and 1). I therefore have 6 different cumulative incidence curves:

Dear R users, I created a matrix that tells me the first day of use of a category by id. #Calculate time difference test$tdiff<-as.numeric(difftime(as.Date("2002-09-01"), test$ftime, units = "days")) # obtain the index date per person and dcategory index.date.test<-tapply(test$tdiff, list(test$id, test$rcat), max) Nonetheless, at the moment I think will be

Full_Name: Ulrike Groemping Version: 2.9.0 (and older) OS: Windows Submission from: (NULL) (84.190.173.190) When using an older version of R, packages are not found although they are available for newer versions of R and do work when installed with the old version. For example, installing DoE.base on R 2.8.1 installs version 0.2, while CRAN is at version 0.4-1 currently. It would be nice if the