similar to: exact logistic regression

Hi I am a new R user and I did try elrm package to do Exact Logistic regression for my dataset ( about 170 patients with a binary dependent variable against age and 6 other binary covariates), I did not have n column because it was about individuals, I made n column with 1 for each individual, here is my code and result: > > #### Libraries needed #### > >

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Conditional logistic regression and small sample sizes: what to do? Dear R-Gurus, [I run R 2.8.0 on WinXP. I have no formal training in statistics.] Please feel free to skip the 'Blah blah part' for questions below. - ---Blah blah part-------- Why I use clogit (package 'survival')?: I am looking for risk factors for cases during a

Dear Colleagues I have been fitting some multinomial logistic regression models using R (version 1.6.1 on a linux box) and Stata 7. Although the vast majority of the parameter estimates and standard errors I get from R are the same as those from Stata (given rounding errors and so on), there are a few estimates for the same model which are quite different. I would be most grateful if

Dear all, I am trying to replicate some multilevel models with binary outcomes using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. The data can be found at <http://www.uni-koeln.de/~ahf34/xerop.dta>. The relevant Stata output can be found at <http://www.uni- koeln.de/~ahf34/stataoutput.txt>. First, you will find the unconditional model,

Hi, The following code, from Angelo Canty article on line "Resampling Methods in R: the boot Package, 2002", works fine for Angelo Canty using R 2.6.0 on Windows XP. It also works for me using R 1.2.1 and S-PLUS 2000 on Windows XP after installing the S-PLUS bootstrap library, with slight differences in my outputs. > library(boot) > library(survival) >

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

I'm developing a package that comes with a data set called RutgersMapB36. One of the package's functions requires this data frame. A toy example is: test<-function() { data(RutgersMapB36) return(RutgersMapB36[,1]) } R CMD check returns a NOTE: test: no visible binding for global variable 'RutgersMapB36' Is there any way to avoid this NOTE? Thanks, Brad --- Brad

Hi to all R community Does anybody know good R packages/books dedicated to efficient usage of R in pharmacoepidemiological research? Thank you in advance -- Denis Kazakiewicz <d.kazakiewicz at gmail.com>

Hello I simply want to plot two variables against one 'year' variable in qplot. Is any way of doing this without reshaping data in long format and using facet function afterwards? Thank you Denis

>On Wed, 3 Aug 2005, Bernd Weiss wrote: > >> I am trying to replicate some multilevel models with binary outcomes >> using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. > >That's not going to happen as they are not using the same criteria. the glmmPQL and lmer both use the PQL method to do it ,so can we get the same result by

Dear ALL, I'm trying to figure out what the percentage effects are in a logistic regression. To be more clear, I'm not interested in the effect on y of a 1-unit increase in x, but on the percentage effect on y of a 1% increase in x (in economics this is also often called an "elasticity"). For example, if my independent variables are in logs, the betas can be directly

Dear All, I am trying to reproduce the example that I found online here http://bit.ly/11VG4ha However, when I run my script (pasted at the end of the email), I notice that there is a factor 2 between the values for the coefficients for the categorical variable female calculated by my script and in the online example. Any idea about where this difference comes from? Besides, how can I

Hi, ?? Are there any packages which prints out p-values for OLR's (like `ologit' from Stata)? I want to run a bunch of OLRs and print the p-value for the first coefficient from each of them. ? I checked polr() under MASS and it doesn't. ?There's a lrm() function under Design which does print out p-values but I couldn't extract p-values from the output. ? Thanks, ? Debs

Dear R people Could you please help with following Trying to compare accuracy of tumor size evaluation by different methods. So data looks like id true metod1 method2 ... 1 2 2 2.5 2 1.5 2 2 3 2 2 2 etc. Could you please give a hint how to deal with that. Seems like {merror} does not suite to me because I am trying to compare accuracy of measurements with their true known values not just

Hi, I have a dataset with the binary outcome Y(0,1) and 4 covariates (X1,X@,X#,X$). I am trying to use MCMClogit to model logistic regression using MCMC. I am getting an error where it doesnt identify the covariates ,although its reading in correctly. The dataset is a sample of actual dataset. Below is my code: > ####################### > > > #retreive data > # considering four

Dear All, I have a MCMC result in x1. I was wondering if there is a simpler, more elegant way of evaluating the estimate of an integral then this (I am pretty sure there is): Also if I want to count the x's say -1<x<2 but not the ones in the burn in period. [code] z <- -2 burnin <- 2000 int1 <-

I am receiving the above error ( full r session output below) the script runs OK in windows. and "genotypes" and "ploidy" are both correct arguments any suggestions would be most welcome Nevil Amos MERG/ACB Monash University School of Biological Sciences > library(Geneland) Loading required package: RandomFields Loading required package: fields Loading required

Deal all, as MCMClogit does not allow for the specification of several chains, I have run my model 3 times with different random number seeds and differently dispersed multivariate normal priors. For example: res1 = MCMClogit(y~x,b0=0,B0=0.001,data=mydat, burnin=500, mcmc=5500, seed=1234, thin=5) res2 = MCMClogit(y~x,b0=1,B0=0.01,data=mydat, burnin=500, mcmc=5500, seed=5678, thin=5) res3 =

Hello: Is there a function that will do exact logistic regressions along the line of LogXact? Thanks, ANDREW

Dear R folks, We completed a matched case-control study that was analyzed using conditional logistic regression. Because of the small sample size we need to calculate exact confidence intervals. The quick solution is to purchase LogExact by Cytel. However, we'd like to do this in R. Anyone have experience with this? Many thanks, Tomas ____________________________________ Tomas Aragon, MD,