similar to: Simulating from the null distribution of a 2 x 3 table

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 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

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]]

R Help on 'chisq.test' states that "if 'simulate.p.value' is 'TRUE', the p-value is computed by Monte Carlo simulation with 'B' replicates. In the contingency table case this is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are positive... In the

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,

hi, is there a way of calculating of measuring dependence between two categorical variables. i tried using the chi square test to test for independence but i got error saying that the lengths of the two vectors don't match. Suppose X and Y are two factors. X has 5 levels and Y has 7 levels. This is what i tried doing >temp<-chisq.test(x,y) but got error "the lengths of the two

The standard chisq.test() and fisher.test() functions, when applied to two distributions (to determine whether the same underlying distribution applies to both) requires one to pre-bin the distributions. Is there a library function (either built-in or in a package) that acts more like the ks.test() function, in that one can simply pass the two distributions and have it do the necessary binning as

I am currently testing species co-occurrence patterns using null models and the oecosimu() function within the vegan() package. My issue is that none of the methods appear to be the ones that I want. The methods listed are r0, r1, r2, r2dtable, swap, tswap. However, I want to know how to go about implementing fixed row algorithms, as suggested in Gotelli 2000 in Ecology. Also, the null models

I have a dataset where I have missing times (11:00 and 16:00). I would like the outputs to include the missing time so that the final time vector looks like "realt" and has the previous time's value. Ex. If meas at time 15:30 is 0.45, then the meas for time 16:00 will also be 0.45. meas are the measurements and times are the times at which they were taken. meas<-runif(18)

I am trying to develop a prognostic model using logistic regression. I built a full , approximate models with the use of penalization - design package. Also, I tried Chi-square criteria, step-down techniques. Used BS for model validation. The main purpose is to develop a predictive model for future patient population. One of the strong predictor pertains to the study design and would not

Hallo I am teaching a very elementary intro course about R, and want to show the students how to find the distribution of the maximum of rolling two balanced die. Is there perhaps a more elegant way to do this, other than the way I am using below? (I would like to show them two ways - the one shown here, and perhaps and improved/elegant approach (showing off the "beauty" of R.

Hi Dear all, I have some gene expression data samples from different tissue types ----------------------------------------------- - 120 samples from blood (B) - 20 samples from Liver (L) - 15 samples from Kidney (K) - 6 samples from heart (H) ----------------------------------------------- All the samples are from different individuals, so there are in total 161 individuals from which the DNA was

Full_Name: Reginaldo Constantino Version: 2.8.0 OS: Ubuntu Hardy (32 bit, kernel 2.6.24) Submission from: (NULL) (189.61.88.2) For many tables, chisq.test with simulate.p.value=TRUE gives a p value that is obviously incorrect and inversely proportional to the number of replicates: > data(HairEyeColor) > x <- margin.table(HairEyeColor, c(1, 2)) >

Hello everyone, Can someone tell me exactly how to generate data from a null distribution for the fisher exact test? I know I have to use the hypergrometric but exactly what commands do I use? Jim [[alternative HTML version deleted]]

I have the following contingency table dat <- matrix(c(1,506,13714,878702),nr=2) And I want to test if their is an association between events A:{a,not(a)} and B:{b,not(b)} | b | not(b) | --------+-----+--------+ a | 1 | 13714 | --------+-----+--------+ not(a) | 506 | 878702 | --------+-----+--------+ I am worried that prop.test and chisq.test are not valid given the

Hello, I'm looking for a textbook that can explain some of the math behind the intro-to-intermediate stuff like ANOVA, multiple regression, non- parametric tests, etc. A little background: I took an intro stats course last year and would like to further my education. Being as that was the highest (and only) stats class the local community college offers, it looks like I'm on

I want to calculate chi square test of goodness of fit to test, Sample coming from Poisson distribution. please copy this script in R & run the script The R script is as follows ########################## start ######################################### No_of_Frouds<- c(4,1,6,9,9,10,2,4,8,2,3,0,1,2,3,1,3,4,5,4,4,4,9,5,4,3,11,8,12,3,10,0,7) N <- length(No_of_Frouds) # Estimation of

Dear R People, This question has nothing to do with R directly, but it is a simulation question. I need to generate a random variable distributed as gamma(\alpha,\beta), with the additional proviso that it must be a function of random variable(s) which do not depend on \alpha, \beta. In this case, I have \alpha = (T-1)/2, where T is a positive integer. So, it seems reasonable to first simulate

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 list I have a vector of values that allegedly have a chi-squared distribution. I want to create a plot that shows the values I have obtained, and the chi-squared distribution curve for the specified number of degrees of freedom to show what should have been obtained. At the moment I am plotting the values I have obtained as a histogram and somehow want to put on to this plot the