similar to: Ordinal logistic regression p-values

Dear R listers, I am learning the MLE utility optim() in R to program ordered logit models just as an exercise. See below I have three independent variables, x1, x2, and x3. Y is coded as ordinal from 1 to 4. Y is not yet a factor variable here. The ordered logit model satisfies the parallel regression assumption. The following codes can run through, but results were totally different from what I

Hi All, I am working on some ordinal logistic regresssions using LRM in the Design package. My response variable has three categories (1,2,3) and after using the creating my model and using a call to predict some values and I wanted to use a simple .5 cut-off to classify my probabilities into the categories. I had two questions: a) first, I am having trouble directly accessing the

Hello, I'm estimating an ordered logit model on a probability weighted survey sample. polr permits case weights with the "weights" option, but I cannot figure out from existing documentation what it actually does with these weights. I'm concerned about this because I get somewhat different results using Stata's ologit command with the pweights option and very

Hi, I am trying to fit a model with an ordered response variable (3 levels) and 13 predictor variables. The sample has complex survey design and I've used 'svydesign' command from the survey package to specify the sampling design. After reading the manual of 'svyglm' command, I've found that you can fit a logistic regression (binary response variable) by specifying the

Hi, I am running an analysis with an ordinal outcome and I need to run a test of the parallel regression assumption to determine if ordinal logistic regression is appropriate. I cannot find a function to conduct such a test. >From searching various message boards I have seen a few useRs ask this same question without a definitive answer - and I came across a thread that indicated there is no

Hi everyone, If I compute a "Ordered Logistic or Probit Regression" with the polr function from MASS package. the summary give me : coefficients, Standard error and Tvalue.. but not directly the p.value. I can compute "manualy" the Pvalue, but Is there a way to directly obtain the pa.value, and I wonder why the p.valeu is not directly calculated, is there a reason? exemple

Hi everyone, I'm a bit new here (and new to R), and I was trying to do an OLR, and testing the parallel slope assumption seems be very important. I browsed through past postings, and didn't find much to help me in this area. I was wondering if anyone knew how I could go about doing this. Thank you. -- View this message in context:

Hi All, I have a whole lot of *.raw files in my working folder and I am doing the same analysis on each of those and want to save all the results in a single file. I am making some mistake here and can't figure out how to solve it. Say, the *.raw files are ABCD.raw, EFGH.raw, IJKL.raw ... The files are of this format ID PHI?? aa1? aa2? aa3 .... 1??? 1???? 1.3?? 2.0?? 1.0 2??? 0????

This is a follow up to the message I posted 3 days ago about how to estimate mixed ordinal logit models. I hope you don't mind that I am just pasting in the code and comments from an R file for your feedback. Actual estimates are at the end of the post. ### Subject: mixed ordinal logit via "augmented" data setup. ### I've been interested in estimating an ordinal logit model

Hi All, I have a data set of around 17,000 gene names and their lengths. E.g.           gene      kblength     A3GALT2       14.333    AADACL3       12.609    AADACL4       22.532         ABCA4     128.312        ABCB10      42.114          ABCD3    100.287     ............ ......... and I was able to draw a reverse cumulative frequency plot using the following code: d <-

Hi, I'm new to R, and I'm STATA user before, could you help me where I can get document about comparison command between STATA and R. Thank you very much, Best regards, -iip-

Hello, ? Are there some basic things one can do to speed up a R code? I am new to R and currently going through the following situation. ? I have run a R code on two different machines. I have R 2.12 installed on both. ? Desktop 1 is slightly older and has a dual core processor with 4gigs of RAM. Desktop 2 is newer one and has a xeon processor W3505 with 12gigs of RAM. Both run on Windows 7. ?

I want to test whether the proportional odds assumption for an ordered regression is met. The UCLA website points out that there is no mathematical way to test the proportional odds assumption (http://www.ats.ucla.edu/stat//R/dae/ologit.htm), and use graphical inspection ("We were unable to locate a facility in R to perform any of the tests commonly used to test the parallel slopes

Hello list, Is there any mean to fit models for ordinal response other than multinomial polytomous ("multinom" from nnet ) and cumulative logit ("polr" from MASS)? I am particularly interested in continuation-ratio model and adjacent-category logit model. It is for the sake of epidemiology in wild-living populations! Many thanks, Emmanuelle Fromont

Hi, ? I am very new to R. So, pardon my dumb question. I was trying to write my own function to run a different model (perform an ordered logistic regression) using the example in website http://pngu.mgh.harvard.edu/~purcell/plink/rfunc.shtml But R returns a error `R Error in eval(expr, envir, enclos) : object 's' not found' when I run it. What am I doing wrong here? Here's

A student brought this question to me and I can't find any articles or examples that are directly on point. Suppose there are 2 ordinal logistic regression models, and one wants to set them into a simultaneous equation framework. Y1 might be a 4 category scale about how much the respondent likes the American Flag and Y2 might be how much the respondent likes the Republican Party in America.

Hello, I am searching for an R-Package that does an exentsion of the Mantel-Haenszel test for ordinal data as described in Liu and Agresti (1996) "A Mantel-Haenszel type inference for cummulative odds ratios". in Biometrics. I see packages such as Epi that perform it for binary data and derives a varaince for it using the Robbins and Breslow variance method. As well as another pacakge

Hi, I'm trying to fit a proportional ordinal logistic model using function polr() (package MASS). Is there a way to fix certain betas in the regression (e.g. function arima() allows this by defining fixed ) Maybe there is another function than polr() which allows that? Thanks Kazys [[alternative HTML version deleted]]

Dear all, I have been using the lrm function in R to run an ordinal logistic regression and I am a bit confused about the methods for checking the model assumptions. I have produced residual plots in R of the score.binary type which I think look ok. However, the partial type plots show bell shaped patterns and have crossing lines, indicating violation of parallelism. However, I noticed

Hi, ?I am working with the following list of files: [1] "study_chr1.one.phased.impute2.chunk1"?????????????? [2] "study_chr1.one.phased.impute2.chunk1_info"????????? [3] "study_chr1.one.phased.impute2.chunk1_info_by_sample" [4] "study_chr1.one.phased.impute2.chunk1_summary"?????? [5] "study_chr1.one.phased.impute2.chunk1_warnings"?????? The