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Hello, I am hoping someone can clarify why I might obtain a quite different value in R & SPSS for a McNemar test I ran. Firstly, here is the R syntax & output R OUTPUT > mctest <- as.table(matrix(c(128,29,331,430), + ncol =2, dimnames = list(group=c("preMHT","postMHT"), + assault=c("yes","no")))) > mctest assault

Hi friends, I get different values for McNemar's test in R and SPSS. Which one should i rely on when the p values differ. I came across this problem when i started learning R and seriously give up on SPSS or any other proprietary software. Thank u in advance Output in SPSS follows *Crosstab* hsc Total ABN NE ABN tvs ABN Count 40 3 43 Row % 93.0% 7.0%

Does anyone know of another version of the Mcnemar test that provides: 1. Odds Ratios 2. 95% Confidence intervals of the Odds Ratios 3. Sample probability 4. 95% Confidence intervals of the sample probability Obviously the Odds Ratios and Sample probabilities are easy to calculate from the contingency table, but I would appreciate any help on how to calculate the confidence

Hello all, I wish to perform a mcnemar test for a 3 by 3 matrix. By running the slandered R command I am getting a result but I am not sure I am getting the correct one. Here is an example code: (tt <- as.table(t(matrix(c(1,4,1 , 0,5,5, 3,1,5), ncol = 3)))) mcnemar.test(tt, correct=T) #And I get: McNemar's Chi-squared

Hello Everyone,   I've been asked to check if there is a significant difference in the following:                     Pre        Post Group A     15/19     14/19 Group B     14/19     10/19   My sense is that I need to perform McNemar's test on these data because responses are correlated within patient from the pre-test to the post-test.   The question is how to do this. I'm a SAS user

I am appealing to the general collective wisdom of this list in respect of a statistics (rather than R) question. This question comes to me from a friend who is a veterinary oncologist. In a study that she is writing up there were 73 cats who were treated with a drug called piroxicam. None of the cats were observed to be subject to vomiting prior to treatment; 12 of the cats were subject to

Hello dear R help group. My question is statistical and not R specific, yet I hope some of you might be willing to help. *Experiment settings*: We have a list of subjects. each of them went through two tests with the answer to each can be either 0 or 1. *Goal*: We want to know if the two experiments yielded different results in the subjects answers. *Statistical test (and why it won't

Hello Everyone, My name is Thomas and I have been using R for one week. I recently found your site and have been able to search the archives of posts. This has given me some great information that has allowed me to craft an initial design to an inquiry I would like to make into the breakdown of McNemar's test. I have read an intro to R manual and the posting guides and hope I am not violating

I am perfectly aware that this question is not an R question, at least not yet, but I have not succeeded in finding what I want in other ways, so ... What I am looking for are two algorithms, preferabley in Pascal, but other languages may do. For (a) systematic (complete) permutations for grouped data with unequal group sizes, and (b) random permutations for the same kind of data. I know

The other day I was looking into one of the classics in resampling, Eugene Edgington's "Randomization Tests". This type of test is simple to do in R with things like a simple correlation, the sample () function is perfect for the purpose. However, things are more complex if you have grouped data, like a one-way ANOVA. The reason is that you have to avoid the consideration of

Hi R experts, I am trying to do a prognostic model validation study, using cancer survival data. There are 2 data sets - 1500 cases used to develop a nomogram, and another of 800 cases used as an independent validation cohort. I have validated the nomogram in the original data (easy with the Design tools), and then want to show that it also has good results with the independent data using 60

Estimados Para analizar tres muestras cualitativas independientes se utiliza Cochran-Mantel-Haenszel test ?Cual script es el adecuado para analizar tres variables dependientes? en MCNEMAR solo se permite analizar dos variables cualitaticvas dependientes saludos José [[alternative HTML version deleted]]

Hi, I have optimized the shrinkage parameter (GCV)for ridge and got my r2 value is 70% . to check the sensitivity of the result, I did permutation test. I permuted the response vector and run for 1000 times and draw a distribution. But now, I get r2 values highest 98% and some of them more than 70 %. Is it expected from such type of test? *I was under impression that, r2 with real data set

I want learn permutation test and resampleing, etc. There are a few references listed below. I'm wondering what is the best book on this topic. Can somebody give me some advice. Thank you! http://en.wikipedia.org/wiki/Resampling_%28statistics%29#Permutation_test

Hello sir: How can I perform McNemar (paired chi square test) by using R? Thanks! ------------------------------ ******************************************* Xin Meng Capitalbio Corporation National Engineering Research Center for Beijing Biochip Technology BioPharma-informatics & Software Dept. Research Engineer Tel: +86-10-80715888/80726868-6438 Fax: +86-10-80726790 Email??xmeng

Hello all I wish to perform a mcnemar.test() for a 5X5 matrix. Wikipedia tells me (http://en.wikipedia.org/wiki/Cochran_test) I should turn to cochran.test. The only place I found it was in the "outliers" package, but the command cochran.test() acts differently then mcnemar.test() , and doesn't take a table as input. Any ideas on how to use it ? #Example code: aa =

i have a data frame(dat) which has many variables.and i use the following script to get the crosstable. >danx2<-c("x1.1","x1.2","x1.3","x1.4","x1.5","x2","x4","x5","x6","x7","x8.1","x8.2","x8.3","x8.4","x11",

I am working with the mcnemar.test function and the help does not show a maintainer/author, but it is part of the stats package. My issue is that I want to use the test on 2 variables with possible values of 0:3, in one of the tests one of the variables does not have any 3's, so to make sure that the matrix is square I do: > x <- factor(x, levels=0:3) > y <- factor(y, levels=0:3)

Hi all, I'm wondering if there's a way that I can perform McNemar's test for a small dataset easier. I know this may be different from the usual way, but I'm hoping to throw it out there for help. For now, my orginnal dataset looks something like this... Chemical shifts Pre_yes Pre_no Post_yes Post_no 1 20 23 30

Hello R users, I would like to perform a mc nemar test. There is my data : __________________________________ x <- matrix(4:1,2,2) x [,1] [,2] [1,] 4 2 [2,] 3 1 __________________________________ My population is too small for normal approximation. __________________________________ library(concord) mcnemar.mh(x) $statistic [1] 0.2 $p [1] 0.6547208 Warning message: low