similar to: Fisher exact test?

Good Afternoon, My name is Gabriel, I'm doing an analysis if there is increase or decrease in dependence on the mutated genes, using 3 or more genes using the fisher exact test.I performed with success an analysis for two genes using fisher.test( ). example of the 2x2 contigency table: Gene A mutated | Gene A normalGene B mutated| 26

i have 2 groups i want to compare: group A and group B each group contains let's say 20 patients i want to perform a Fisher Exact test on genotype distribution so, see if there is a sign diff in genotpe frequency/distribution (#AA, #AB, #BB) between group A and B not for 1, but for 1000 different genes my question: how should i build my table so i can do: test <-

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

Hi, Sorry for a basic questions on linear models. I am trying to adjust raw data for both fixed and mixed effects. The data that I output should account for these effects, so that I can use the adjusted data for further analysis. For example, if I have the blood sugar levels for 30 patients, and I know that 'weight' is a fixed effect and that 'height' is a random effect, what

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

I'm using fisher.exact on a 4x2 table and it seems to work. Does anyone know exactly what is going on? I thought fisher.exact is only for 2x2 tables. Note: I can't use chi-squared because I have a couple of cells with 0 and < 5 observations. -- View this message in context: http://r.789695.n4.nabble.com/fisher-exact-for-2x2-table-tp3481979p3481979.html Sent from the R help

Hi, My apologies for the naive question! I have three overlapping sets and I want to find the probability of finding a larger/greater intersection for 'A intersect B intersect C' (in the example below, I want to find the probability of finding more than 135 elements that are common in sets A, B & C). For a two set problem, I guess I would do a Fisher or chi-square test. Here is what

Hello! I have this problem: I want to create a Venn's diagram with three lists of genes'names. The first is all the genome, the second a subset of it comprising all mitochondrial genes, and the third including all genes that correlate with a given gene. This is what I do: > library(gplots) > A<-read.delim("F:/.../mito genes just names.txt") >

Hi all, I have been struggling with this problem for a few days. I have a data table like this: gene rpkm1 diff1 rpkm2 diff2 gene1 23 50 13 120 gene2 111 220 827 1200 gene3 75 998 71 910 And I want to re-format it so that, for each gene, I have a 2x2 contingency table, such as: gene rpkm diff gene1 23 50 gene1 13 120 gene2 111 220 gene2 827

Hi: Apologies for asking the following question. As?this may sound very basic and stupid for this forum?, I honestly do not know how to solve it and I do not have a teacher who can help me understand. ? I have list of genes (200)?that are involved in a particular process and I call this as a?ProcSet.?? From an independent experiment I found that out of 10,000 genes, 1500 are significant and I

Hello, Is there a test for independence available based on a multidimensional contingency table? I've about 300 processes, and for each of them I get numbers for failures and successes. I've two or more conditions under which I test these processes. If I had just one process to test I could just perform a fisher or chisquare test on a 2x2 contigency table, like this: for one process:

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

Hi All. Sorry to bother you. I have a question about fisher exact test. I counted the presence of gene mutation in two groups of samples. My data is as follows Presence Absence GroupA 4 6 GroupB 5 11 When using the formula of fisher exact test provided by wiki (http://en.wikipedia.org/wiki/Fisher%27s_exact_test), the p-value is 0.29. But when

This mean First, I am no expert but I am analyzing some marketing data. I have information on two versions of the same site, and I have data on the number of times people filled out a form on each version of the site. Sample data: Site 1 Site 2 Filled out form 10 35 Did not fill out form 50

Hello R help members. I came across today with an article on Barnard's exact test (http://www.cytel.com/Papers/twobinomials.pdf), that is supposed to give a more powerful fisher.test - Because it doesn't assume that we know the row and column totals are in advance. Any pointers to such a function ? Thanks, Tal -- ---------------------------------------------- My contact information:

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

I'd like to plot a point at the intersection of these two curves. Thanks x <- seq(.2, .3, by = .01) f <- function(x){ x*cos(x)-2*x**2+3*x-1 } plot(x,f(x), type = "l") abline(h = 0)

The following contingency table generates p-value > 1 from fisher.test() ff = c(0,10,250,5000); dim(ff) = c(2,2); fhisher.test(ff)$p.value Sean [[alternative HTML version deleted]]

The following contingency table generates p-value > 1 from fisher.test() ff = c(0,10,250,5000); dim(ff) = c(2,2); fhisher.test(ff)$p.value Sean [[alternative HTML version deleted]]

I do not understand how to interpret this to find the T Value for the data. Is there a way to figure this out, or another function that will provide this for me using Fisher's Exact Test? The outcome of my data is listed below. data: DATA p-value = 0.1698 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 0.6026805 79.8309210 sample estimates: