search for: ologits

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

...; "3" "4" "5" "6" converting to numeric and back doesn't seem to help: > levels(as.factor(as.numeric(miset1$facts_convict))) [1] "1" "1" "2" "3" "4" "5" "6" It's messing up my ologits. ?Any way to correct this?

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, 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

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, ?? 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

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, 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-

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

I've run an ordered logistic regression model in R with Zelig and am looking to calculate predicted probabilities. Zelig has a series of simple one line commands to generate the information I want on first differences and so forth. Unfortunately, I keep getting an error when running the zelig function and was wondering if there was a quick alternative for generating predicted probabilities for