similar to: Simulating the p-value of a test statistic

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:

Hi all, I am a biostatistician and I have developed my own ranking system for clinical data. I would like to test the efficiency of it w.r.t. to other ranking systems. I would like to simulate the data and after assigning ranks to my observed scores(after neglecting dropouts), observe the type I error. If I want to do a Kruuskal Wallis type of test, what test statistic should I use to test for a

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)) >

Dear all, I calculate the test statistic for the KS test outside R, and wish to use R only to calculate the corresponding p-value. Is there a way for doing this? (as far as I see, ks.test() requires raw data as input). Alternatively, is there a way to provide the ks.test() the two CDFs (two samples test) rather than the (x, y) data vectors? Thanks in advance, Rani

Dear R-Users, I want to use mtable from package "memisc" to produce Latex-style estimation output. However, mtable() only gives me a p-value and not the corresponding test-statistic. Does anyone know how to extract it, either from a glm/anova object or mtable? Here is a short example: # Run this #################### install.packages("memisc") library(memisc) set.seed(1)

Hello, I am interested in using Morans I for different time intervals to detect disease clusters. Ultimately I would like to use CUSUM - or similar monitoring statistic to monitor the results of Morans I - similar to the work by Rogerson (2005) Spatial Surveillance and Cummulative Sum Methods in Spatial and Syndromic Surveillance for Public Health. Thus far - thanks to the list I have

Hi, Is the array mstats returned by virDomainMemoryStats call cummulative or snapshot of the counter at that particular time ? I was of the opinion that is cummulative like the /proc counters but this counter seems to go backwards ie next snapshot value is less than the current one which makes me think these are snapshot values . I'm using RHEL 6.3 libvirt version 0.9.10. Regards,

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

I have inherited centos 6.3 and 6.2 vms in an esxi environment. When I do yum provides ntpd on the 6.3 box I get a lot of errors like: Loaded plugins: fastestmirror, presto Loading mirror speeds from cached hostfile * base: mirror.unl.edu * extras: mirrors.cmich.edu * updates: mirror.steadfast.net http://mirror.unl.edu/centos/6.4/os/x86_64/repodata/repomd.xml: [Errno 14] PYCURL ERROR 22 -

Hi, I am looking for some assistance with these requirements. We are trying to simulate web requests to hit a web applications. Let's assume I have a url to hit that requires a username and password. 1. These web requests should have exponential inter arrival times. 2. Generate self-similar traffic. I am not very sure about the properties of this traffic. Is Variance Time plot

Hi, I have some servers that are dedicated to do meetme conferencing. From some previous test i concluded that I need to use dahdi_dummy as it is more accurate. If I did use the core timers in dahdi (not loading dahdi_dummy) I got bad quality in the conferences and dahdi_test showed 99.6% as worst. I thought maybe the issue as bad hardware for the timing or something else. But today I

hi, just having the idea create a simple tcl/tk gui-dialog for different data-file formats i get starting problems and it would be nice get some tips/tricks from experienced tcl/tk user in R ! tt <- tktoplevel() label.widget <- tklabel(tt,text="Decision Tree GUI") button.widget <- tkbutton(tt,text="Select SPSSFile", command=function()

Hello, I want to compute the P-value from the joint cumulative distribution of an n-dimensional order statistic in R, using the formula found on http://cmgm.stanford.edu/%7Ekimlab/multiplespecies/Supplement/methods_network.html My data consists of three different techniques (G2D, POCUS and RANDOM), and each has associated with it a number of rankings (integer between 0 and 1000), like for

Hi, I conduct a univariate regression with lm function. I would like to get the p value for the regression. Is there a method that would enable me to extract the p value into a variable. Thanks. Arjun Bhandari ************************************************************************************************************** This email and any files transmitted with it are confidentia...{{dropped}}

Hi, Is anyone aware of a rank-based, non-paired test such as the Krustal-Wallis, that can accommodate weights? Alternatively, would it make sense to simulate a dataset by duplicating observations in proportion to their weight, and then using the Krustal-Wallis test? thanks! Dylan

Hello, All: I want to simulate future observations from fits to heteroscedastic data. A simple example is as follows: (DF3_2 <- data.frame(y=c(1:3, 10*(1:3)), gp=factor(rep(1:2, e=3)))) # I want to fit 4 models # and simulate future observations from all 4: fit11 <- lm(y~1, DF3_2) fit21 <- lm(y~gp, DF3_2) library(nlme) (fit12 <- lme(y~1, data=DF3_2,

Sir, I am working on fitting distribution on multivariate financial data and then simulate observations from that fitted distribution. I use stepAIC.ghyp() function of 'ghyp' library which select the best fitted distribution from generalized hyperbolic distribution class on the given dataset. data(indices) # Multivariate case: aic.mv <- stepAIC.ghyp(indices, dist =

Dear All   I know that you do not have to help me (as this is not a pure R problem) but please do, i am new to R as a CPI compiler, i just need to do a sample to see which sampling method best works in different situations, therefore since this is for practice purposes nobody will finance a real project thats why i need you to help me direct me as to how simulate data (just direct me,not 100%

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]]

Dear List, Does anyone know how to simulate data from a GLM object correponding to values of the independent (x) variable that do not occur in the original dataset? I have tried using simulate(), but it generates a new value of the dependent variable corresponding to each of the original x-values, which is not what I need. Ideally I whould like to simulate new values for GLM objects