similar to: chisq.test() and r2dtable() freezing on certain inputs (PR#5701)

>>>>> "Torsten" == Torsten Hothorn <torsten@hothorn.de> >>>>> on Thu, 11 Dec 2003 18:03:07 +0100 (CET) writes: Torsten> On Thu, 11 Dec 2003, Jeffrey Chang wrote: >> Hello everybody, >> >> I'm running R 1.8.1 on both Linux and OS X compiled with >> gcc 3.2.2 and 3.3, respectively. The following

On Thu, 11 Dec 2003, Jeffrey Chang wrote: > Hello everybody, > > I'm running R 1.8.1 on both Linux and OS X compiled with gcc 3.2.2 and > 3.3, respectively. The following call seems to freeze the interpreter > on both systems: > > chisq.test(matrix(c(233, 580104, 3776, 5786104), 2, 2), > simulate.p.value=TRUE) > > By freeze, I mean, the function call never

Hello everybody, I'm running R 1.8.1 on both Linux and OS X compiled with gcc 3.2.2 and 3.3, respectively. The following call seems to freeze the interpreter on both systems: > chisq.test(matrix(c(233, 580104, 3776, 5786104), 2, 2), simulate.p.value=TRUE) By freeze, I mean, the function call never returns (running > 10 hours so far), the process is unresponsive to SIGINT (but I

Hello, While doing some enrichment tests using chisq.test() with simulated p-values, I noticed some strange behaviour. The computed p-value was extremely small, so I decided to dig a little deeper and debug chisq.test(). I noticed then that the simulated statistics returned by the following call tmp <- .Call(C_chisq_sim, sr, sc, B, E) were all the same, very small numbers. This, at first,

>>>>> Gustavo Fernandez Bayon <gbayon at gmail.com> >>>>> on Thu, 24 Aug 2017 16:42:36 +0200 writes: > Hello, > While doing some enrichment tests using chisq.test() with simulated > p-values, I noticed some strange behaviour. The computed p-value was > extremely small, so I decided to dig a little deeper and debug >

> On 25 Aug 2017, at 11:23 , Jari Oksanen <jari.oksanen at oulu.fi> wrote: > > It is not about "really arge total number of observations", but: > > set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11) > > A11 > 0 1 2 > 166483 666853 166664 > > There are

It is not about "really arge total number of observations", but: set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11) A11 0 1 2 166483 666853 166664 There are three possible matrices, and these come out in proportions 1:4:1, the one with all cells filled with ones being most common. Cheers, Jari

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

Hi, the following example aiming at a class containing a logical slot segfaults under R-1.9.0 when `gctorture(on = TRUE)' is used: Code code (dummy.c): #include <Rdefines.h> SEXP foo() { SEXP ans; PROTECT(ans = NEW_OBJECT(MAKE_CLASS("test"))); SET_SLOT(ans, install("lgl"), allocVector(LGLSXP, 1)); LOGICAL(GET_SLOT(ans,

> On 25 Aug 2017, at 10:30 , Martin Maechler <maechler at stat.math.ethz.ch> wrote: > [...] > https://stackoverflow.com/questions/37309276/r-r2dtable-contingency-tables-are-too-concentrated > > >> set.seed(1); system.time(tabs <- r2dtable(1e6, c(100, 100), c(100, 100))); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1)) > user system elapsed >

Hello R-users, I would like to generate two-way contingency tables with zero in one cell. I tried to use the function r2dtable but I could not force one cell to have zero value. Any Idea? Best regards.. Mahmoud [[alternative HTML version deleted]]

Dear useRs, a new package for tree-structured regression is available on CRAN. This package implements a unified framework for recursive partitioning which embeds tree-structured regression models into a well defined theory of conditional inference procedures. Stopping criteria based on multiple test procedures are implemented. The methodology is applicable to all kinds of regression problems,

Dear useRs, a new package for tree-structured regression is available on CRAN. This package implements a unified framework for recursive partitioning which embeds tree-structured regression models into a well defined theory of conditional inference procedures. Stopping criteria based on multiple test procedures are implemented. The methodology is applicable to all kinds of regression problems,

Dear useRs, `multcomp' version 0.991-1 will be shortly available from CRAN near you. Nearly all functionality contained in the package has been re-implemented from scratch. The focus of the package has been extended to general linear hypotheses in arbitrary parametric models and the most important function to check out is `glht()'. Multiple comparison of means procedures (for example

Dear useRs, `multcomp' version 0.991-1 will be shortly available from CRAN near you. Nearly all functionality contained in the package has been re-implemented from scratch. The focus of the package has been extended to general linear hypotheses in arbitrary parametric models and the most important function to check out is `glht()'. Multiple comparison of means procedures (for example

Drs. Brian Everitt and Torsten Hothorn will present their online course "Modeling in R" at statistics.com Jan. 19 - Feb. 16. Participants can ask questions and exchange comments with Drs. Everitt and Hothorn via a private discussion board throughout the period. In this course you learn how to use R to build statistical models and use them to analyze data. Multiple regression is

Conditional Inference Procedures in a Permutation Test Framework The `coin' package implements a general framework for conditional inference procedures, commonly known as permutation tests, theoretically derived by Strasser & Weber (1999). The conditional expectation and covariance for a broad class of multivariate linear statistics as well as the corresponding multivariate limiting

Conditional Inference Procedures in a Permutation Test Framework The `coin' package implements a general framework for conditional inference procedures, commonly known as permutation tests, theoretically derived by Strasser & Weber (1999). The conditional expectation and covariance for a broad class of multivariate linear statistics as well as the corresponding multivariate limiting

I don't use SPSS but following through on your detective work can provide the likely answer. First note that both date numbers are evenly divisible by the number of seconds in a day, i.e. 24*60*60. This suggests that these numbers are seconds since some origin. Since we know "2003/02/11" corresponds to 13264300800 we deduce that the origin must be spss.orig <-