similar to: Simulating contingency table (Basic question, help please)

Hello R-users, I would like to generate two-way contingency tables with zero in one cell. I tried to use the function r2dtable but I could not force one cell to have zero value. Any Idea? Best regards.. Mahmoud [[alternative HTML version deleted]]

This is a basics beginner question. I attempted fitting a a Poisson GLM to data that is non-integer ( I believe Poisson is suitable in this case, because it is modelling counts of infections, but the data collected are all non-negative numbers with 2 decimal places). My question is, since R doesn't return an error with this glm fitting, is it important that the data is non-integer. How does

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

Dear all, I want to simulate from the null distribution of the following 2 x 3 table, 2 5 10 4 8 5 I am using a chi-squared test. Anyone has any idea how to do this? -- Thanks, Jim. [[alternative HTML version deleted]]

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,

It is not about "really arge total number of observations", but: set.seed(4711);tabs <- r2dtable(1e6, c(2, 2), c(2, 2)); A11 <- vapply(tabs, function(x) x[1, 1], numeric(1));table(A11) A11 0 1 2 166483 666853 166664 There are three possible matrices, and these come out in proportions 1:4:1, the one with all cells filled with ones being most common. Cheers, Jari

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

Dear Listers: I have a categorical feature selection problem for random forest. Suppose I have a multiple-leveled category variable A, which has m=3 levels: red, green, and blue and the final target is binary classification. I want to evaluate its power in discrimination between 2 classes. We know rf splits multiple-leveled category variable by considering all combinations of its levels. So

I have a contingency table with a 0/1 variable (fwr) that flags a particular condition, a weight variable (cnt) and other variables (e.g. zz) characterizing that observation of the table. I am trying to use GLM. By converting the variables to factors and running the following regression: fwr1<-factor(fwr) zz1<-factor(zz1) res1<-glm(fwr1 ~ zz1,weights=cnt) Is that appropriate? Paul M.

Hello All, Is there any R function out there to turn a multi-way contingency table back to a flat file table of individual rows and attribute columns.? Thanks! marco --------------------------------- [[alternative HTML version deleted]]

Hi all, I have a 3x4 contingency table with row totals all being 100. I want to generate 3 x 4 tables from the null distribution. Which R function can do this? -- Thanks, Jim. [[alternative HTML version deleted]]

Dear R-philes: I am having an issue with exporting contingency tables with xtable(). I set up a contingency and convert it to a matrix for passing to xtable() as shown below. v.cont.table <- table(v_lda$class, grps, dnn=c("predicted", "observed")) v.cont.mat <- as.matrix(v.cont.table) Both produce output as follows: observed predicted uh uh~ uh 201

Hello, Is there anywhere I can find some Bayesian Code for 2 by 2 tables or even the non-central hypergeometric distribution in R? Packages would be helpful but the actual coding in R is much better. Thanks, Thanks Jim [[alternative HTML version deleted]]

Dear R-list, Hi everyone, Im trying to make an analysis of multidimensional contingency tables using R. I' working with the Agresti example where you have the data from 3 categories. The thing is how can I do the analisys using the G2 statistics. Somebody can send me an Idea? I attach the program where you can find the data. Best Regards, > prob1<-

Hi all, is it possible to perform a linear-by-linear association test (lbl_test, package coin) for a SINGLY ordered table ? And, if yes, how to manage the "scores" ? Thanks in advance Gilles - France [[alternative HTML version deleted]]

Hi, I would like to do a multiple correspondence analysis with the mca function in the MASS library on data that I have as a contingency table (which I've tried converting to a data frame). For example, ========= > data(HairEyeColor) > hair.df <- as.data.frame(HairEyeColor) > hair.df Hair Eye Sex Freq 1 Black Brown Male 32 2 Brown Brown Male 38 3 Red

Hi, all. I got a lot of help from the this mailing list, it save me a lot of time. Here is my question. x<-rep(c(2,3,4),20) y<-sample(rep(c(0,1),30)) tr<-rep(c(1:5),6) data1<-data.frame(x,y,tr) data1<-data1[order(data1$tr),] > data1 x y tr1 2 0 16 4 1 111 3 0 116 2 0 121 4 0 126 3 1 131 2 0 136 4 1 141 3 0 146 2 0 151 4 0 156 3 0 12 3 0 27 2 0

How can I draw a 3D perspective of a contingency table, that is showing 3D bars whose height is in proportion of the table cells? --------------- Charles RAUX, Laboratoire d'Economie des Transports CNRS-Universit? Lumi?re Lyon 2-ENTPE email : charles.raux at let.ish-lyon.cnrs.fr http://www.ish-lyon.cnrs.fr/let -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

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)}