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...gs.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, indeed the algorithm seems start from the expected value of a cell entry and then "explore from there"... and I do wonder...

Hi, I am not sure which kind of test is applied to the data if you use cor.test(x, y) ? Is it an unpaired t-Test? Regards -- View this message in context: http://www.nabble.com/cor.test%28x%2Cy%29-tp22492993p22492993.html Sent from the R help mailing list archive at Nabble.com.

...s.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, indeed the algorithm seems start from the expected value of a cell entry and then "explore from there"... and I do wonder...

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,

...ug.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, indeed the algorithm seems start from the > expected value of a cell entry and then "explore from th...

...e prediction error at time zero. The reference for getQ0 (cited by help(arima)) is: Gardner, G, Harvey, A. C. and Phillips, G. D. A. (1980) Algorithm AS154. An algorithm for exact maximum likelihood estimation of autoregressive-moving average models by means of Kalman filtering. _Applied Statistics_ *29*, 311-322. where it is called subroutine STARMA (and coded in fortran 77). My problem is that getQ0 returns incorrect covariance matrices in certain cases. Indeed, below is an example of a SARIMA(1,0,1)x(1,0,0)_12 where getQ0 gives a covariance matrix which possess negative e...

...> variance, and is approximately normally distributed. > > For Spearman's test, p-values are computed using algorithm AS 89. > > References: > > D. J. Best & D. E. Roberts (1975), Algorithm AS 89: The Upper Tail > Probabilities of Spearman's rho. _Applied Statistics_, *24*, > 377-379. > > Myles Hollander & Douglas A. Wolfe (1973), _Nonparametric > Statistical Methods._ New York: John Wiley & Sons. Pages 185-194 > (Kendall and Spearman tests). > > > On Fri, Mar 13, 2009 at 5:37 AM, mentor_ <mentor_@g...