similar to: Tests on contingency tables

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, how can I "reshape"/"cast" the following matrix 00;01;10;11 John.Mike;123;313;12;31 John.Jim;54;57;39;36 John.Steve;135;47;47;74 Mike.Jim;63;37;27;16 Mike.Steve;15;15;5;61 Jim.Steve;6;10;34;35 into a set of stacked 2x2 contingency tables 0;1 John;123;12 Mike;313;31 John;54;39 Jim;57;36 John;135;47 Steve;47;16 ... so that the "fisher.test" and

I'd like to carry out a Monte Carlo simulation test where given data is a contingency table. I think this is something to do with using rmultinonom(), but I'm not sure how to code this, to simulate contingency tables. Could anyone please help with how to use R to simulate contingency tables like this? -- View this message in context:

Dear List: Most of the analysis I do involves contingency tables. I am migrating to R from Stata and I have a number of questions about using contingency tables in R. I suspect that most of the things I want to do are very short R scripts that people on this list probably have. I wonder if you would be willing to share them. First, the presentation of tables by table() is not

Hello all, Let say I have 2-way contingency table: Tab <- matrix(c(8, 10, 12, 6), nr = 2) and the Chi-squared test could not reject the independence: > chisq.test(Tab) Pearson's Chi-squared test with Yates' continuity correction data: Tab X-squared = 1.0125, df = 1, p-value = 0.3143 However I want to get all possible contingency tables under this independence

Hi, I have a table in R with data I needed and need to create a contingency table out of it. The table I have so far looks like this: Binger r DietType No Yes Dangerous 15 12 Healthy 52 9 None 134 24 Unhealthy 72 23 These are the error messages that I keep getting whenever I try to get a contingency table. I'm not sure why it won't work

Hello there! I am still struggling with a binomial response over all categorical variables (some of them with 3 levels, most with 2 levels). After initial struggles with glm's (struggle coming from the data, not the actual analysis) I have decided to prefer contingency tables. I have my data such as: response:

Hi All, I have a question regarding contingency tables. I would like to calculate the mean and standard deviation of a continuous variable from my own dataset based on the percentages of a contingency table I obtained from a scientific article. dataset<- data.frame(cbind(case=rep(0:1,5), x1=c(1:10), x2=c(0:9))) contingency_table<- matrix(c(100, 75, 65, 85, 90, 87), nrow=3, ncol=3) In

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

I am trying to create contingency tables (to evaluate prior to performing Pearson's Chi-Squared test for independence). I would like to see column and row totals as well as expected and observed values and cell counts. I tried to use the package "contingency. tables" but get the following warning: package ‘contingency.tables’ is not available (for R versions 2.15.2) Is there

Hello! May you help me? I'm trying to do a contingency table using this > data(iris) > library(rpart) > modelo <- rpart(Species ~., iris) > prev <- predict(modelo, iris) Finally the contingency table > table(iris$Species, prev) But an error occurs: Error in table(iris$Species, prev) : all arguments must have the same length And I do not understand why, may you

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

Dear all Seems that puzzles always come in packs. I was asked to help with some statistics in blood analysis. (You can not refuse your wife's asks :-). She has contingency table for values IgVH mutation and ZAP expression. I can do chi-square test (in R) and get a results, and with some literature I can try explain them. However she found an article in which they use SPSS and use

Hi, Found no measure of association or correlation for 2x2 contingency tables in fullrefman.pdf or google. Can someone point to a package that implements such calculations? Thanx. -- Alexandre Santos Aguiar - consultoria para pesquisa em sa??de - R Botucatu, 591 cj 81 tel 11-9320-2046 fax 11-5549-8760 www.spsconsultoria.com

I have had the same problem and I wrote this function rmulti <- function(n, size, p) { NrDim <- length(p) if(NrDim<2) stop("The simulated variabel has to be at least 2-dimensional") res <- matrix(data=NA, nrow=n, ncol=NrDim) p <- p/sum(p) TempSize <- size for(i in 1:NrDim) { TempP <- p[i]/sum(p[i:NrDim]) TempBin <- rbinom(n=n, size=TempSize,

hello everyone, i often work on contingency table that I create from data.frame (with table() function) but a friend sent me an excel sheet wich *already is* a contingency table (just a simple 2 way table !...) any clue on how to import it in R (keeping row names and col names) ? any tuto I come accross only mention the table transformation, but never the import of such data I only found

Dear Friends. I am just starting to use R. And in this occasion I want to construct a high-dimensional contingency table, because I want to crate a mosaic plot with the vcd package. My table is in this format: año ac.rep cat.gru conteos 1 2005 R parejas 253 2 2005 N parejas 23 3 2006 R parejas 347 4 2006 N parejas 39 5 2007 R

Dear List, I would appreciate help on the following matter: I am aware that higher dimensional contingency tables can be analysed using either log-linear models or as a poisson regression using a generalized linear model: log-linear: loglm(~Age+Site, data=xtabs(~Age+Site, data=SSites.Rev, drop.unused.levels=T)) GLM: glm.table <- as.data.frame(xtabs(~Age+Site, data=SSites.Rev,

Hello, How can I create a symmetric contingency table from two categorical vectors having different length of levels? For example one vector has 98 levels TotalData1$Taxa.1 [1] "Aconoidasida" "Actinobacteria (class)" "Actinopterygii" "Alphaproteobacteria" [5] "Amoebozoa"

Hello, I know there must be a simple soluton to this problem but it eludes me currently. My data is partitioned into two subsets, each subset has a common column factor but with varying levels: levels(fdf_ghc$AgeDemo) [1] "26TO35" "36TO45" "46TO55" "56TO65" "66TO75" "76TO85" levels(fdf_ghcnull$AgeDemo) [1] "26TO35"