similar to: fisher exact vs. simulated chi-square

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

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,

> 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]]

I am currently testing species co-occurrence patterns using null models and the oecosimu() function within the vegan() package. My issue is that none of the methods appear to be the ones that I want. The methods listed are r0, r1, r2, r2dtable, swap, tswap. However, I want to know how to go about implementing fixed row algorithms, as suggested in Gotelli 2000 in Ecology. Also, the null models

> 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

>>>>> 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 >

>>>>> "MM" == Martin Maechler <maechler@stat.math.ethz.ch> >>>>> on Thu, 11 Dec 2003 18:29:05 +0100 (CET) writes: >>>>> "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:

Dear all, I want to simulate from the null distribution of the following 2 x 3 table, 2 5 10 4 8 5 I am using a chi-squared test. Anyone has any idea how to do this? -- Thanks, Jim. [[alternative HTML version deleted]]

I'd like to carry out a Monte Carlo simulation test where given data is a contingency table. I think this is something to do with using rmultinonom(), but I'm not sure how to code this, to simulate contingency tables. Could anyone please help with how to use R to simulate contingency tables like this? -- View this message in context:

Dear useRs I am looking for a way to randomise the values within a matrix: the conditions are that the sums of the rows and the sums of the columns should remain the same as in the original matrix. Any help would be appreciated Cheers Yann

This may sound crazy but... I have data like this... > results.matrix [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 949 93 2 11 26 20 7 6 10 5 0 3 [2,] 1233 124 24 35 58 57 17 21 31 19 11 21 Which is the result of binning (summing) the response variables of an underlying (nearly) continious range of predictor

Please, I would like to generate a random bistochastic matrix, that is a squared matrix of non-negative numbers with each row and each column sum to 1, for example : .2 .3 .5 .6 .3 .1 .2 .4 .4 I don't know of to code this. Do you have any idea ? Thanks Florent Bresson ___________________________________________________________________________ Demandez ?

Hello, I am trying to find a permutation test that works on a general nxm table. The data set is small enough to have cells with too small counts to make chi2-approximation invalid. If the table was a 2x2 contingency table I would like to use a Fsher exact test (fisher.test) but that wont work in this general table. Does there exist a general function for this test. Best regards, Magnus

I am considering testing: Ho: Odds Ratio =1 H1: Odds Ratio <>1 How can I generate data from the null distribution for a specific configuration of a Fisher exact test? Jim [[alternative HTML version deleted]]

Hi, This message is to let the R developers know that the pdfs for the first two volumes of the printed edition of R reference manual are available at, http://www.network-theory.co.uk/R/base/vol1.pdf http://www.network-theory.co.uk/R/base/vol2.pdf I've divided the manual into "core commands" of the base package (control flow, and related programming commands, etc) in Volume 1

I would like to be able to randomise presence-absence (i.e. binary) matrices whilst keeping both the row and column totals constant. Is there a function in R that would allow me to do this? I'm working with vegetation presence-absence matrices based on field observations. The matrices are formatted to have sites as rows and species as columns. The presence of a species on a site is

The build system rolled up R-4.0.3.tar.gz (codename "Bunny-Wunnies Freak Out") this morning. The list below details the changes in this release. You can get the source code from https://cran.r-project.org/src/base/R-4/R-4.0.3.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. Binaries for various platforms will appear in due course. For the R Core Team, Peter

The build system rolled up R-4.0.3.tar.gz (codename "Bunny-Wunnies Freak Out") this morning. The list below details the changes in this release. You can get the source code from https://cran.r-project.org/src/base/R-4/R-4.0.3.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. Binaries for various platforms will appear in due course. For the R Core Team, Peter

The build system rolled up R-4.0.3.tar.gz (codename "Bunny-Wunnies Freak Out") this morning. The list below details the changes in this release. You can get the source code from https://cran.r-project.org/src/base/R-4/R-4.0.3.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. Binaries for various platforms will appear in due course. For the R Core Team, Peter