search for: patefield

chisq.test with simulate.p.value=TRUE uses the Patefield algorithm, this is not documented, and the original reference is not given, as it ought to be. The reference is: Patefield,W. M. (1981) An efficient method of generating r * c tables with given row and column totals (algorithm AS 159). Applied Statistics 30, 91-97. Kjetil Halvorsen

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

Dear all, I have a question about the chisq.test command. As an option one can chose the computation of p-values by Monte-Carlo simulation (simulate.p.value=T). Is there any documentation available how this calculations are done and how this simulation based test behaves in small samples? Thanks Klaus Abberger University of Konstanz, Germany [[alternate HTML version deleted]]

...in spite of the fact that I had spent quite some time to fix such problem in Dec 2003, see the 14 years old bug PR#5701 https://bugs.r-project.org/bugzilla/show_bug.cgi?id=5701#c2 It has to be said that this is based on an algorithm published in 1981, specifically - from help(r2dtable) - Patefield, W. M. (1981) Algorithm AS159. An efficient method of generating r x c tables with given row and column totals. _Applied Statistics_ *30*, 91-97. For those with JSTOR access (typically via your University), available at http://www.jstor.org/stable/2346669 When I start reading it,...

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:

...n spite of the fact that I had spent quite some time to fix such problem in Dec 2003, see the 14 years old bug PR#5701 https://bugs.r-project.org/bugzilla/show_bug.cgi?id=5701#c2 It has to be said that this is based on an algorithm published in 1981, specifically - from help(r2dtable) - Patefield, W. M. (1981) Algorithm AS159. An efficient method of generating r x c tables with given row and column totals. _Applied Statistics_ *30*, 91-97. For those with JSTOR access (typically via your University), available at http://www.jstor.org/stable/2346669 When I start reading it,...

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,

...spent quite some time to fix > such problem in Dec 2003, see the 14 years old bug PR#5701 > https://bugs.r-project.org/bugzilla/show_bug.cgi?id=5701#c2 > > It has to be said that this is based on an algorithm published > in 1981, specifically - from help(r2dtable) - > > Patefield, W. M. (1981) Algorithm AS159. An efficient method of > generating r x c tables with given row and column totals. > _Applied Statistics_ *30*, 91-97. > > For those with JSTOR access (typically via your University), > available at http://www.jstor.org/stable/2346669 >...

hie all i am trying to carry out a categorical data analysis but my problem is that when in i use the chi squared test some of my expected values are less than 5. is there a test that can handle this situation. the data is not a 2*2 table. its more from the social sciences where you have from strongly agree to strongly disagree. i know i can collapse vthe tables but there is a loss of

...faster for large matrices (using an optimized BLAS) but are less flexible. o QR objects now have class "qr", and solve.qr() is now just the method for solve() for the class. o New function r2dtable() for generating random samples of two-way tables with given marginals using Patefield's algorithm. o rchisq() now has a non-centrality parameter `ncp', and there's a C API for rnchisq(). o New generic function reorder() with a dendrogram method; new order.dendrogram() and heatmap(). o require() has a new argument, character.only, -- to make it align w...

...faster for large matrices (using an optimized BLAS) but are less flexible. o QR objects now have class "qr", and solve.qr() is now just the method for solve() for the class. o New function r2dtable() for generating random samples of two-way tables with given marginals using Patefield's algorithm. o rchisq() now has a non-centrality parameter `ncp', and there's a C API for rnchisq(). o New generic function reorder() with a dendrogram method; new order.dendrogram() and heatmap(). o require() has a new argument, character.only, -- to make it align w...