similar to: - McNemar with unequal sample sizes

Hi, I wonder why factor scores produced by factanal are correlated, and I'd appreciate any hints from people that may help me to get a deeper understanding why that's the case. By the way: I'm a psychologist used to SPSS, so that question my sound a little silly to your ears. Here's my minimal example: *********************************************** v1 <-

Dear R-List, to assess the model fit for 2PL-models, I tried to mimic the bootstrap-approach chosen in the GoF.rasch()-function. Not being a statistician, I was wondering whether the following simplification (omit the "chi-squared-expressed model fit-step") would be appropriate: GoF.ltm <- function(object, B = 50, ...){ liFits <- list() for(i in 1:B){ rndDat <-

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 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 =

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

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 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%

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- I have run an ANOVA on 4 treatments with unequal sample sizes (n=9,7,10 and 10). I want to determine where my sig. differences are between treatments using a Bonferroni test, and have run the code: pairwise.t.test(Wk16, Treatment, p.adf="bonf") I receive an error message stating that my arguments are of unequal length: Error in tapply(x, g, mean, na.rm = TRUE) :

I am using levelplot() from lattice with grids that have unequal cell sizes. This means that the boundary between two cells is not always half-way between nodes, as levelplot() assumes. The result is that some cell sizes are rendered incorrectly, which can be painfully obvious if using relatively large cells. Is there any work-around? I am using the conditioning capability of lattice and

Hello, I am hoping someone could shed some light on power calculations for me. I have two small data sets of unequal sample size after NA removal (m = 5, f = 7). m <- c(2.0863, 2.1340, 2.1008, 1.9565, 2.0413, NA, NA) f <- c(1.8938, 1.9709, 1.8613, 2.0836, 1.9485, 2.0630, 1.9143) In a R help message/reply from Sep 30, 2001, it was noted that the "power.t.test" function

Dear R helpers   Suppose e.g. I have a csv file having three variables defined and each of these variables have data items of say 40, 50, 45 length. When I open this csv file in 'R', I get 10 trailing 'NA's under first column and 5 'NA' s in case of 3rd column.   How do I get rid of these NA's s.t. when I read the first column, there should be only 40 data items, 2nd

Power calculations two sample test for proportions is very useful. Is there a way however, to get away from the two samples being of the same size. What would happen if one had n=15 in the one sample and n=45 in the other sample. Farrel Buchinsky, MD Pediatric Otolaryngologist Allegheny General Hospital Pittsburgh, PA **********************************************************************

# users would benefit from a warning about the behavior in the # groups.unbalanced case below. A propogation of difference is the variance # calculation is leading to an apparently significant difference in # means, even though the numeric values are all identical. Obviously upon # inspection the difference in means is meaningless, but users might be warned # if all of the input data is

I hope this question is adequate for this list I use the nnet code from V&R p. 348: The very nice and general function CVnn2() to choose the number of hidden units and the amount of weight decay by an inner cross-validation- with a slight modification to use it for classification (see below). My data has 2 classes with unequal size: 45 observations for classI and 116 obs. for classII With

Hi, all I have a rather un-ideal dataset that I am trying to work with, and would appreciate any advice you have on the matter. I have 4 years worth of data taken at 3 depth-zones from which samples have been taken at random. I am looking at the abundance of organism A between depth zones and across years, and am interested in the possible interaction of organism A distributions shifting

My apologies for not including the transcript in the original message: > xx <- rep(2.10,80) > groups.balanced <- as.factor(c(rep("one",40),rep("two",40))) > groups.unbalanced <- as.factor(c(rep("one",44),rep("two",36))) > t.test(xx ~ groups.balanced) Welch Two Sample t-test data: xx by groups.balanced t = 0, df = 78, p-value = 1

Hi, I am trying a two way anova with unequal sample sizes but results are not as expected: I take the example from Applied Linear Statistical Models (Neter et al. pp889-897, 1996) growth rate gender bone development 1.4 1 1 2.4 1 1 2.2 1 1 2.4 1 2 2.1 2 1 1.7 2 1 2.5 2 2 1.8 2 2 2 2 2 0.7 3 1 1.1 3 1 0.5 3 2 0.9 3 2 1.3 3 2 expected results are