similar to: Simulating n 2x2 tables with the same odds ratio

I'm looking for a way to generalize the calculation of (log) odds ratios for 2 x 2 x {strata} frequency tables in vcd::oddsratio() to R x C x {strata} tables. For an R x C table, F, odds ratios can be defined in several ways; one simple way, particularly for tables with ordered factors, is to calculate the set of continuation odds ratios, based on the (R-1)x(C-1) set of 2x2 tables with

Hello. I'm allready this far. I have a function which is calculating the lower (l) and upper (u) limit for a confidence interval for the odds ratio. For example for 5 simulated 2x2 tables the upper and lower limits are: > u [1] 2.496141 7.436524 8.209161 4.313587 3.318612 > l [1] -0.9718608 1.1000713 1.5715373 0.1135158 -0.2700517 With (l[1]; u[1]) being the confidence interval

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

mantelhaen.test() gives the exact conditional p-value (for independence) and confidence intervals (CIs)for the common odds ratio for a stratified 2x2 table. The epitools package by Tomas Aragon (available via CRAN) contains functions which use fisher.test() to calculate mid-p exact p-values and CIs for the CMLE odds ratio for a single 2x2 table. The mid-p p-value for independence for a stratified

I'm wondering how odds ratio is computed. I thought that it is (n11/n12)/(n21/n22), but it is not what fisher.test() computes. Could somebody let me know? > n11=3 > n12=1 > n21=1 > n22=3 > > n1_=n11+n12 > n2_=n21+n22 > > n_1=n11+n21 > n_2=n12+n22 > > x=rbind(c(n11,n12),c(n21,n22)) > > threshold=dhyper(n11,n1_,n2_,n_1) >

Hello: Please help me through my confusion. I am having trouble reconciling the difference between what I believe is the conventional definition of an odds ratio for a 2-by-2 table and the output produced by fisher.test() in R. Consider the following example: > Discrim <- matrix(c(1,10,24,17), + nr = 2, + dimnames = list(AGE = c('young', 'old'), +

Hi list, I am trying to calculate the sensitivity, specificity, and odds ratio(and confidence interval). Say, my data looks like this 42.53, 37.56, 40.51, 32.67, 38.19, 81.74, 41.55, 68.94, 59, 63, 54.13, 48.85, 50.46, 51.78 Is there any packages in R that does this? Espeically the odds ratio confidence interval. Mike [[alternative HTML version deleted]]

Hi, I am new to R. Anyone can explain the following from R-help or anyone can direct me how to calculate odds ratio from logistic model in R. Thank you very much. Guoya Stefano <stecalza at tiscalinet.it <https://stat.ethz.ch/mailman/listinfo/r-help> > writes: >Hi all. > >A simple question. >Is there a function to compute the Odds Ratio and its confidence intervall,

Hi friends, I have a doubt reg the application of odds ratio. When evaluating a diagnostic test, what odds ratio I should apply? When I use.. *>oddsratio (X)** * the OR I get is not ad/bc. Thank you. Regards, Dr Manoj Aravind, PG Resident, Dept of Community Medicine, Osmania Medical College, Hyderabad, India. [[alternative HTML version deleted]]

Dear all, is there a automatic method to obtain Odds Ratio estimates and their confidence intervals from a GLM Logistic model? Thanks Cristian ============================================= Cristian Pattaro ============================================= Unit of Epidemiology & Medical Statistics Department of Medicines and Public Health University of Verona cristian@biometria.univr.it

HI there, i know this is a basic question, though i need some help because this is somewhat away from my current issue, but nevertheless interesting to me... Lets assume i have some estimated probabilities, say estimated by a logit model. i know i can also state them as an odds ratio. Now i?d like to state these odds ratios as a reference to a specific outcome of my investigated

Dear all, How can i obtain odds ratio in logistic regression when the model contains interaction terms in R? how can i obtain OR for a special case? Thanks in advance for any help. Amin

Hi, does anyone know of a function to calculate odds ratios in multiway tables (stratified) (+ the other usual statistics involved) i mean: say we have a table r*c*d, For every d (depth) we have a r*c table, and in this table the odds ratio's are calculated for every 2*2 subtable in it. logically this function would look like): ORs(multiwaytable) or ORs(data$var1r,data$var2c,data$var3d)

Dear all, I have been trying to find a function to calculate the Breslow-Day test for homogeneity of the odds ratio in R. I know the test can be preformed in SAS but i was wondering if anyone could help me to perform this in r. In addition i have the fullrefman file to search for functions in the basic R packages, does anyone have any suggestions of an efficient way of searching for

Hi all. A simple question. Is there a function to compute the Odds Ratio and its confidence intervall, from a logistic model (glm(.......,family=binomial....). I've written my own, but certainly someone did a better job. Thank you in advance, Stefano *********************************************** Stefano Calza Istituto di Statistica Medica e Biometria Universit? degli Studi di Milano Via

Say I fit a logistic model and want to calculate an odds ratio between 2 sets of predictors. It is easy to obtain the difference in the predicted logodds using the predict() function, and thus get a point-estimate OR. But I can't see how to obtain the confidence interval for such an OR. For example: model <- glm(chd ~age.cat + male + lowed, family=binomial(logit)) pred1 <-

Peter & Michael, I just came across the following on another mailing list and realized that my use (and the authors of the article use of the term 'odds ratio' ) is probably incorrect. I believe my interest is in the 'odds' of schizophrenia among the population of homicide, rather than a comparison of odds . "Yours seems a good idea, Kevin, if you are only interested

Dear All, I am learning the ropes about logistic regression in R. I found some interesting examples http://bit.ly/Vq4GgX http://bit.ly/W9fUTg http://bit.ly/UfK73e but I am a bit lost. I have several questions. 1) For instance, what is the difference between glm.out = glm(response ~ poverty + gender, family=binomial(logit), data=mydata) and glm.out = glm(response ~ poverty * gender,

Dear list, I made a logistic regression model (MyModel) using lrm and penalization by pentrace for data of 104 patients, which consists of 5 explanatory variables and one binary outcome (poor/good). Then, I found bootcov and robcov function in rms package for calculation of confidence range of coefficients and odds ratio by bootstrap covariance matrix and Huber-White sandwich method,

Hi, I want to do a global likelihood ratio test for the proportional odds logistic regression model and am unsure how to go about it. I am using the polr() function in library(MASS). 1. Is the p-value from the likelihood ratio test obtained by anova(fit1,fit2), where fit1 is the polr model with only the intercept and fit2 is the full polr model (refer to example below)? So in the case of the