similar to: extracting the residuals from models working with ordinal multinomial data

Hi, how can I do multinomial logistic regression in R? I think glm() can only handle binary response variable, and polr() can only handle ordinal response variable. how to do logistic regression with multinomial response variable? Thanks __________________________________

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

Dear all, I am new to R and would like to run a multinomial logistic regression on my dataset (3 predictors for 1 dependent variables) I have used the vglm function from the VGAM package and got some results. Using the predict() function, I obtained the probability table I was looking for. However, I would like to fully understand how the predict() function generates the probabilities or in

Hi all. I'm just starting to explore ordinal multinomial regression. My dataset is 300,000 rows, with an outcome (ordinal factor from 1 to 9) and five independent variables (all continuous). My first stab at it was this: pomod <- polr(Npf ~ o_stddev + o_skewness + o_kurtosis + o_acl_1e + dispersal, rlc, Hess=TRUE) And that worked; I got a good model fit. However, a variety of other

Hello All, I'm trying to run a multinomial probit on a dataset with 28 data points and five levels (0,1,2,3,4) in the latent choice involving response variable. I downloaded the latest mnp package to run the regression. It starts the calculation and then crashes the rpogram. I wish I could give the error message but it literally shuts down R without a warning. I'm using the R

Dear all, To modelize the abundance of fish (4 classes) with a set of environmental variables, I used the polr and predict.polr functions. I would like to know how to bring the cumulated probabilities back to a discrete ordinal scale. For the moment I used the predict.polr function with the argument "class". Is there an other way? polrf <- polrf <- polr_mod(formula =

Dea all, Let's suppose I am studying a questionnaire survey and one of the questions has three ordered categorical responses (say, A, B and C). Eg result<-ordered(c(rep("A",12),rep("B",37),rep("C",6))) Assume the respondents are not grouped. The differences between the subsequent levels can be, I think, modeled with polr: fit<-polr(result~1) summary(fit)

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.

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, I am trying to fit a ordinal GEE model using ordgee {geepack}. In order to check the validity of the function, I specified the correlation structure as independence (i.e. constr = "independence") and compared the result with that using polr {MASS}. Because a GEE model with an independent working correlation structure is equivalent to an ordinary GLM model, we would expect the same

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]]

Hi, there, Is there a R routine which can fit multinomial logistic regression for nominal outcomes? Not the multinom() of log-linear model, neither the polr() for ordinal outcomes. Thanks. Jun Han

I want to fit a multinomial model with logit link. For example let this matrix to be analyzed: male female aborted factor 10 12 1 1.2 14 14 4 1.3 15 12 3 1.4 (this is an example, not the true data which are far more complex...) I suppose the correct function to analyze these data is polr from MASS library. The data have been

Hi! I'm looking for a solution to analyse data, which consists of dichotomous responses (yes/no) for 2 multinomial ordinal variables. I was trying glm() and got hierarhical models treating all variables as nominal, but I can't figure out how to tell glm() to use a model for ordinal data like this: log(Mij) = intercept + X + Y + Z + beta*(x-x')*(y-y') where beta is a

R-Experts: I am using the vglm function of the VGAM library to perform proportional odds ordinal logistic regression. The issue that I would like help with concerns the format in which the response variable must be provided for this function to work correctly. Consider the following example: ------ library(VGAM) library(MASS) attach(pneumo) pneumo # Inspect the format of the original dataset

Dear Peter. I guess you know that Jim Lindseys code include nordr and ordglm in library gnlm - I attach the htmls which do various linear and nonlinear ordinal regressions - exemplified with just the data mentioned, McCullagh (1980) JRSS B42, 109-142. I had it work very fine. -----Oprindelig meddelelse----- Fra: Peter Malewski <p.malewski at tu-bs.de> Til: Troels Ring <tring at

Hi I want to do logistic regression for multinomial data. How can I do it in R? Thanks a lot Nimal Fernando [[alternative HTML version deleted]]

Hi, I am working on a regression tree in Rpart that uses a continuous response variable that is ordered. I read a previous response by Pfr. Ripley to a inquiry regarding the ability of rpart to handle ordinal responses in 2003. At that time rpart was unable to implement an algorithm to handle ordinal responses. Has there been any effort to rectify this in recent years? Thanks! Stacey On

Is it possible to use function "glm" in case when my outcome variable has 5 different classes? I have seen examples only when using binomial outcome variable. What about using function "multinom"? How do I to get the signifigance and the confidence levels of the coefficients and the value of goodness of the model with this function? Thank You for Your help! -- View this

Patrick Lindsey has made available a library devoted to ordinal models available at: http://www.luc.ac.be/~plindsey/publications.html Best wishes Troels Ring -----Oprindelig meddelelse----- Fra: Peter Malewski <p.malewski at tu-bs.de> Til: E. S. Venkatraman <venkat at biost.mskcc.org> Cc: r-help at stat.math.ethz.ch <r-help at stat.math.ethz.ch> Dato: 24. februar 2000 22:48