similar to: Fisher's Exact Test

Timothy, I believe you are mistaken. Fisher's exact test give the correct answer even in the face of small expected values for the cell counts. Pearson's Chi-square approximates Fisher's exact test and can give the wrong answer when expected cell counts are low. Chi-square was developed because it is computationally "simple". Fisher's exact test, particularly with tables

Sorry, but I lost this thread, so I sending this as a new message. This is really a follow-up to a post from a couple days ago saying that fisher.test from the ctest library crashed on the following data set: > T [,1] [,2] [1,] 2 1 [2,] 2 1 [3,] 4 0 [4,] 8 0 [5,] 6 0 [6,] 0 0 [7,] 1 0 [8,] 1 1 [9,] 7 1 [10,] 8 2 [11,]

Is anyone aware of a fast way of doing fisher's exact test for a series of 2 x 2 tables in R? The fisher.test is really slow if n1=1000 and n2 = 1000. -- Thanks, Jim. [[alternative HTML version deleted]]

Hi, I have a text mining project and currently I am working on feature generation/selection part. My plan is selecting a set of words or word combinations which have better discriminant capability than other words in telling the group id's (2 classes in this case) for a dataset which has 2,000,000 documents. One approach is using "contrast-set association rule mining" while the

Hi folks, Forgive me if this question is a trivial issue. I was doing a series of Fishers' exact test using the fisher.test function in stats package. Since the counts I have were quite large (c(64, 3070, 2868, 4961135)), R suggested me to use *other algorithms* for the test which can be specified through the 'control' argument of the fisher.test function as I understood. But where

Hi, I'm try to compute the minimum sample size needed to have at least an 80% of power, with alpha=0.05. The problem is that empirical proportions are really small: 0.00154 in one case and 0.00234. These are the estimated failure proportion of two medical treatments. Thomas and Conlon (1992) suggested Fisher's exact test and proposed a computational method, which according to their table

Full_Name: Martha Nason Version: 2.0.1 OS: Windows XP Submission from: (NULL) (137.187.154.154) I am running simulations using fisher's test on 2 x c tables and a very small p.value from fisher's test (<2.2e-16) is returned as a negative number. Code follows. > set.seed(0) > nreps.outer <-7 > pvalue.fisher <- rep(NA,nreps.outer) > > population1 <- c(

Hi, I'm trying to run Fisher's Exact test on the data below, but I'm not sure how interpret the data shown. Can someone tell me what this is saying? Looking at the numbers it should be that there's no significant difference between the HDL and LDL, but a p-value of 1 seems high. Is the low value in the LDL unbound making the test unstable and should I be using an alternative? Best

Hi, R-help, I have a group of data from RNA-seq want to be analyzed by Fisher's exact test in R. I want to compare the significant difference of about 30,0000 individuals in two different samples, and I have no idea how to use R, so could you please give me some suggestions or the scripts for Fisher's exact test? Thank you very much. Best, Guanfeng Wang [[alternative HTML version

I want to ascertain the basis of the table ranking, i.e. the meaning of "extreme", in Fisher's Exact Test as implemented in 'fisher.test', when applied to RxC tables which are larger than 2x2. One can summarise a strategy for the test as 1) For each table compatible with the margins of the observed table, compute the probability of this table conditional on the

I have a matrix say, 1 4 23 30 and I want to find the previously attainable fisher's exact test p-value. Is there a way to do this in R? -- Thanks, Jim. [[alternative HTML version deleted]]

I am contemplating improving my ctest package as follows: * Add exact p,q,r,s functions for the Wilcoxon distribution, and change the test accordingly (make `exact' work). * Make Fisher's test work for tables larger than 2 by 2. * Perhaps add an exact unconditional test for 2 by 2 tables? * Perhaps add something on estimating/testing relative risk and odds? As clearly I'd like to

Dear All, I have a problem understanding the difference between the outcome of a fisher exact test and a chi-square test (with simulated p.value). For some sample data (see below), fisher reports p=.02337. The normal chi-square test complains about "approximation may be incorrect", because there is a column with cells with very small values. I therefore tried the chi-square with

In an effort to select the most appropriate number of clusters in a mixture analysis I am comparing the expected and actual membership of individuals in various clusters using the Fisher?s exact test. I aim for the model with the lowest possible p-value, but I frequently get p-values below 2.2e-16 and therefore does not get exact p-values with standard Fisher?s exact tests in R. Does anybody know

Has anyone noticed this behavior: > library(ctest) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "J: \STATS\RW1000/library/ctest/libs/ctest.dll": LoadLibrary failure > library(ctest) > version _ platform Windows arch x86 os Win32 system x86, Win32 status major 1 minor 0.0 year 2000 month

Dear friends, My dataset have many zeros, so i must use fisher exact test . Unfortunately, the fisher.test(stats) function fail to do it. Anybody knows how to do the fisher exact test with many zeros in the dataset? My dataset is: a<-matrix(c(0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,1,1,0,2,1,5,1,1,6,4,4,1,17,2,8,5,7,1,1,24,3,6,1,1,3,2,16,7,4,0,2,4,0,17,0,1,0,0,0,1,2),nrow=8,byrow=TRUE) data.frame(a)

Hi, I have a table of observed counts for various genetic markers. Instead of doing Fisher's exact test for each marker one at a time and recording the P value manually, is there a script to go through the whole list and generate the P value column automatically? An example of my data: Counts_CHB and Counts_AA are the observed counts for one allele. 2N_CHB and 2N_AA are the total number

A simple question - using the following fishers test it appears that the P value is significant, but the CI includes 1. Is this result correct? > data.50p10min <- matrix(c(16,15, 8, 24),nrow=2) > fisher.test(data.50p10min) Fisher's Exact Test for Count Data data: data.50p10min p-value = 0.03941 alternative hypothesis: true odds ratio is not equal to 1 95

Full_Name: nobody Version: 2.2.0 OS: any Submission from: (NULL) (219.66.34.183) 2 x 2 table, such as > x [,1] [,2] [1,] 10 12 [2,] 11 13 > chisq.test(x) Pearson's Chi-squared test with Yates' continuity correction data: x X-squared = 0.0732, df = 1, p-value = 0.7868 but, X-squared = 0.0732 is over corrected. when abs(a*d-b*c) <= sum(a,b,c,d), chisq.value

Suppose we have the following data set: Men Women Dieting 10 30 Non-dieting 5 60 If I run the Fisher exact test in R then what does alternative = greater (or less) imply? For example: mat = matrix(c(10,5,30,60), 2,2) fisher.test(mat,alternative ="greater") I get the p-value = 0.01588 and odds ratio = 3.943534. Also, when I flip the rows of