similar to: Cochran’s Q statistic

In SAS, for a two-way (or 3-way, stratified) table, the CMH option in SAS PROC FREQ gives 3 tests that take ordinality of the factors into account, for both variables, just the column variable or neither. Is there an equivalent in R? The mantelhaen.test in stats gives something quite different (a test of conditional independence for *nominal* factors in a 3-way table). e.g. I'd like to

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 want to understand ANOVA better. But a few textbook that I have do not describe Cochran's Theorem in details. Could somebody recommend a book for me?

Sir, I have a problem here while applying chisquare test to the following Data ( below the subject of this mail) ...when I wanted to test the significance using three different free statistical packages, here R, EpiInfo and OpenEpi. *Only OpenEpi accepts the test based on Cochran's Recommendations. * R says " chi squared approximation may be incorrect." Does it mean the same as

Dear all, I am trying to look for a built in function that performs the cochran Q test. that is, cochranq.test(X) where X is a contingency table (maybe a matrix or data.frame). The output will naturally be the test statisitcs, p-value, etc. A quick search on Google gives me the cochran.test in the 'outlier' package, but I had a look at the description of the test and it doesn't look

Hi! I would like to conduct a Cochran`s Q Test in R, but have not found any suitable function. My first idea was: J <- as.table(matrix(c(6,16,2,4),ncol=2, dimnames = list("C" = c("Favorable","Unfavorable"),"Drug A Favorable"=c("B Favorable","B Unfavorable")))) L <- as.table(matrix(c(2,4,6,6),ncol=2, dimnames =

I have a data set like this ID=c("A","A","A","A","A","A","A","B","B","B","B","B","B","B") s=c(1.1,2.2,1.3,1.1,3.1,4.1,4.2,1.1,2.2,1.3,1.1,3.1,4.1,4.2) d=c(1,2,3,4,5,6,7,1,2,3,4,5,6,7) t=c(-3,-2,-1,0,1,2,3,-3,-2,-1,0,1,2,3)

I have a data set like this ID=c("A","A","A","A","A","A","A","B","B","B","B","B","B","B") s=c(1.1,2.2,1.3,1.1,3.1,4.1,4.2,1.1,2.2,1.3,1.1,3.1,4.1,4.2) d=c(1,2,3,4,5,6,7,1,2,3,4,5,6,7) t=c(-3,-1,0,1,2,3,4,-3,-2,-1,0,1,2,3)

Hi, ? Is there some easy way to split the grid.newpage() into two columns? For example, how could I put the two forest plots below (meta1 and meta2) next to each other? library(meta) data(Olkin95) meta1 <- metabin(event.e, n.e, event.c, n.c,data=Olkin95, subset=c(41,47,51,59),sm="RR", meth="I",studlab=author) meta2=meta1 meta2$studlab=rep("",length(meta1$studlab)

Hi, I have been trying to use the function cochran.test from the Outliers package to test for homogeneity of variance. This works well except when I use transformed data. Would anyone have an idea why it doesn't work and how I could do the cochran test on transformed data? Thanks, Stephanie >library(outliers) > set.seed(1234) > x=rnorm(100) >

Hello, I'm new in this R world and I don't know much about statistics, but now I have to analize some data and I've got some first queries yet: I have 5 sets of area mesures and each set has 5 repetitions. My first step is to check data looking for outliers. I've used the outliers package. I have to use the cochran test and the grubbs test in case I find any outlier. The problem

Hello, I have some satellite tag time-at-depth (TAD) frequency data that I would like some help with. The data was transmitted via satellite as percent time spent in each of 7 depth bins (0m, 0-1m, 1-10m, 10-50m etc.), binned over 6-hour intervals. I categorized each row of data corresponding to a date and time into summer vs. winter, and day vs. night, and then summed and averaged the given

Dear R-Community, I'm currently trying to find a way to conduct a meta-analysis in R. I would like to analyze data from mostly-cross-sectional survey-studies. The effect sizes would be correlations. The R packages "meta" and "rmeta" are, as far as I can see, set up for analysis with effect sizes for differences (i.e. comparison of the means/odds-ratios of experimental

Dear List, I am looking for what B.S.Everitt refers to as Cochrane Method for testing independence in combined 2x2 contingency tables. Is it the same method as the Cochran-Mantel-Haenszel Chi-Squared Test for Count Data in R? Thanks, Serguei [[alternative HTML version deleted]]

Hi! I am searching for the Cochran-Armitage-trend-test. Is it included in an R-package? Thank you! --

Dear Sir or Madam, I am trying to plot forest plot. I extracted odds ratio and their corresponding 95% confidence interval from papers, then I calculated the log(OR) and standard error using the following command OR<-metagen(logOR,selogOR,sm="OR") forest(OR,comb.fixed=TRUE,comb.random=TRUE,digits=2) However, it does not produce a forest plot. Can someone kindly help? Thank

Dear sir, I am Shibu John from Thrombosis Research Institute India. It is a multidisciplinary organisation concerned with the interrelated problems of thrombosis and atherosclerosis. I was searching for Cochran armitage trend test program in R. Then I had seen your R coding for C-A trend test. I tried that in the R software. But I can?t run the program due the [Error: could not find function

Hello, I am working on a project. The new data files is coming as the data collectors get data, then the data collectors put these new data files in a folder. I need to read these new data files when they are in folder. so far, I did this job manually, that is to say, each time I go to that folder and find new data files, then use my R program to read these new data files. I am wondering if

Hello! I have some data stored into 2 separate csv file. 1 file (called A.csv) (12 results named Group1, Group2, Group3, etc...) odds ratios, 2 file (called B.csv) 12 corresponded errors. How to import that data into R and make forest plot like I saw inside help file Rmeta and meta with included different font colors and names trough X and Y axis. I know for meta libb ... out <-

It is my understanding, the alloca memory routines are used for forcing variables to be allocated on the stack frame -- which you would want for source level debugging. When SSA registers are used, LLVM will decide what goes into registers and what will spill over to the stack frame. I want the latter. --w Wayne O. Cochran Assistant Professor Computer Science wcochran at vancouver.wsu.edu