similar to: McNemar's Test Question

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%

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

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, 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

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

Hi all, I have updated the exact2x2 package which calculates exact conditional tests and the matching confidence intervals for 2x2 tables. The big change in the update is to allow the option paired=TRUE which gives an exact McNemar's test together with odds ratio estimates and confidence intervals. See vignette("exactMcNemar") for details. The original release is described in the

Hi all, I have updated the exact2x2 package which calculates exact conditional tests and the matching confidence intervals for 2x2 tables. The big change in the update is to allow the option paired=TRUE which gives an exact McNemar's test together with odds ratio estimates and confidence intervals. See vignette("exactMcNemar") for details. The original release is described in the

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 =

Greetings, I have two files which contain responses to a series of multiple choice questions. One file contains responses before an "intervention" and the other contains the responses afterward. There were three possible responses to each question: D, F, T (for Don't Know, False, and True). I would like to try McNemar's test to determine if there was any significant

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)

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

Hi, i need to test the equality of proportions in a paired case, like here: after before + - + 10 14 - 5 53 So usually i use the mcnemar.test(stats). Due to mortality, I now got the problem that my sample sizes in both factors are not equal any more -- there are less cases in the "after" condition. I already learned that Ekbohm (1982) and Marascuilo adressed the problem

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

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 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

Dear list, I have data like this: dat1 <- data.frame(subject=rep(1:10,2), cond1=rep(c("A","B"),each=5), cond2=rep(c("C","D"),each=10), choice=sample(0:1,10,replace=TRUE)) I would like to compare subjects' "choice" for (cond1=="A" & cond2=="C") vs

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]]

What is the exact formula used in R lm() for the Adjusted R-squared? How can I interpret it? There seem to exist several formula's to calculate Adjusted R-squared. Wherry’s formula [1-(1-R2)·(n-1)/(n-v)] McNemar’s formula [1-(1-R2)·(n-1)/(n-v-1)] Lord’s formula [1-(1-R2)(n+v-1)/(n-v-1)] Stein 1-(n-1/n-k-1)(n-2)/n-k-2) (n+1/n) Theil's formula (found here:

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

Hi, I received a bug report for my exact2x2 package that seems to apply to fisher.test in the stats package also. Here is some code which creates the error: >x<-factor(c(0,1,1),levels=c(0,1)) > y<-factor(c(1,1,1),levels=c(0,1)) > fisher.test(x,y) Error in fisher.test(x, y) : 'x' and 'y' must have at least 2 levels > The help says that x and y could be