similar to: chisq test and fisher exact test

Hi, I asked this question before, which was hidden in a bunch of questions. I repharse it here and hope I can get some help this time: I have 2 contingency tables which have the same group variable Y. I want to compare the strength of association between X1/Y and X2/Y. I am not sure if comparing p-values IS the way even though the probability of seeing such "weird" observation under H0

Hi, I want to analyse a contigency table (3 x 12) with a fisher.test beacause there are cells that are less than 5. ?mmen Anken Baf Belchen H?chi Hof Porti R?m Schmutz Sch?n Sissa Tann class14 7 26 150 2 46 68 126 66 3 31 7 61 class24 7 6 55 5 49 71 93 90 1 18 16 79 class34 1 1 4 3 19 8 29 61

Hello all, I would like to know what the difference is between chisq.test and fisher.test when using the Monte Carlo method with simulate.p.value=TRUE? Thank you -- View this message in context: http://r.789695.n4.nabble.com/Difference-in-Monte-Carlo-calculation-between-chisq-test-and-fisher-test-tp2322494p2322494.html Sent from the R help mailing list archive at Nabble.com.

I would compile a table of all the words in the dataset (maybe you have it already), then create a list where each component is an integer vector of indices of words. That is, replace words by their positions in the table. >From that sparse form you could create binary features to use with standard classification methods, or for example compute the X'X matrix for linear regression

l used the following code to generate a sample and then calculated then did a log rank test.can l get the normal version of the logrank eg sqrt of the chisqr(1) will give you the N~(0,1). from my sample can i use the above expression to get the normal dist from the result of the log rank test. thank s=1 while(s!=0){ n=100 m<-matrix(nrow=n,ncol=4)

Hi, there: I have a dataset like the following: 1412|WINDOW|SHATTER|TORN|SOFT|TOP|WATER|RAIN|LAB|AI|BOLL|CAMP|0 1413|PARK|IV|STRUCK|PARK|PUSH|COD|POLICI|CIA|TB|SIC|0 2412|ACCID|REAREND|MULTI|EH|IV|MIDDL|FAN|DUAL|LOSS|CALM|1 2414|IV|REAREND|CD|COG|LAB|ADVERS|1 2415|ACCID|SINGL|VEHICL|IV|SWERV|AVOID|OBJECT|STRUCK|PHONE|POLE|FAN|0 2417|ACCID|SINGL|VEHICL|ROLL|DUE|FATAL|FAN|DUAL|LOSS|CALM|1

I have a 64 bit Gentoo, with a Liebert UPS. The last NUT working version was sys-power/nut-2.4.1-r1 (I also tried sys-power/nut-2.4.3-r2 but downgraded, don't remember the reason). Anyway with the upgrade to sys-power/nut-2.6.0 I started to see this error message when I try to start upsdrv: [root at aemaeth:/etc/nut]$ /etc/init.d/upsdrv start * Starting UPS drivers ... Network UPS Tools -

Hi All, ? I am trying to use the lordif package in R and can't figure out the reason for the above error when I try to run the calctheta() function. ################## # read data using foreign package - must be Stata 11 or earlier racedata<- read.dta("race.dta")??? # item responses resp.data<-racedata[,-c(1:2)] # grouping variable is in the second column

http://bugs.freedesktop.org/show_bug.cgi?id=25527 Summary: The console doesn't work with Nouveau and KMS on a 9400M Product: xorg Version: 7.5 Platform: x86-64 (AMD64) OS/Version: Linux (All) Status: NEW Severity: major Priority: medium Component: Driver/nouveau AssignedTo: nouveau at

The Chisquare test is based upon a normal approx of the (essentially) binomial distribution for the data in question. Small EXPECTED (not observed) values (<5) suggest a asymetric distribution and potential errors in inferential conclusions. The alternative is the exact test, which calculates the exact probabilities of the observed distribution, or a more extreme one, given the constraining

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]]

The chisq.test contains following code: STATISTIC <- sum(sort((x - E)^2/E, decreasing = TRUE)) However, based on book Accuracy and stability of numerical algorithms <http://ftp.demec.ufpr.br/CFD/bibliografia/Higham_2002_Accuracy%20and%20Stability%20of%20Numerical%20Algorithms.pdf> Table 4.1 on page 89, it is better to sort the data in increasing order than in decreasing order, when the

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

Full_Name: Tao Shi Version: 1.7.0 OS: Windows XP Professional Submission from: (NULL) (149.142.163.65) > x [,1] [,2] [1,] 149 151 [2,] 1 8 > c2x<-chisq.test(x, simulate.p.value=T, B=100000)$p.value > for(i in (1:20)){c2x<-c(c2x,chisq.test(x, simulate.p.value=T,B=100000)$p.value)} > c2tx<-chisq.test(t(x), simulate.p.value=T, B=100000)$p.value > for(i in

>>>>> dmurdoch writes: >> Date: Wed, 16 Jul 2003 01:27:25 +0200 (MET DST) >> From: shitao@ucla.edu >>> x >> [,1] [,2] >> [1,] 149 151 >> [2,] 1 8 >>> c2x<-chisq.test(x, simulate.p.value=T, B=100000)$p.value >>> for(i in (1:20)){c2x<-c(c2x,chisq.test(x, >> simulate.p.value=T,B=100000)$p.value)}

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

>>>>> "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

>>>>> "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:

In running the following example of a loglinear model for the Titanic data, I was surprised to see NaN reported for the Pearson chisq > loglin(Titanic, margin=list(1:3, 4)) 2 iterations: deviation 2.273737e-13 $lrt [1] 671.9622 $pearson [1] NaN $df [1] 15 $margin $margin[[1]] [1] "Class" "Sex" "Age" $margin[[2]] [1] "Survived" Tracing it back,

>>>>> Jan Motl writes: > The chisq.test on line 57 contains following code: > STATISTIC <- sum(sort((x - E)^2/E, decreasing = TRUE)) The preceding 2 lines seem relevant: ## Sorting before summing may look strange, but seems to be ## a sensible way to deal with rounding issues (PR#3486): STATISTIC <- sum(sort((x - E) ^ 2 / E,