similar to: ordinal logit

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

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

Dear useR's, I was comparing results for a logistic regression model between different library's. themodel formula is arranged as follows: response ~ (intercept) + value + group OR: glm( response ~ (intercept) + value + group , family=binomial(link='logit')) lrm( response ~ (intercept) + value + group ) ROC( from = response ~ (intercept) + value + group ,

I'm dealing with a classification problem using ordinal logistic regression. In the case of binary logistic regression with unequal proportions of 0's and 1's, a threshold in the interval [0,1] has to be adapted to transform back the predicted probabilities into 0 and 1. This can be done quite straightforward using e.g. the Kappa statistics as accuracy criterion. With

Dear all, I am trying to run a mixed multinomial logit model in R since my response variable has 4 non-ordinal categories. I am using the package mlogit that estimates the parameters by maximum likelihood methods. First of all, I prepared my data using the mlogit.data command. In the mlogit command, one can introduce alternative-specific (fixed factors??) and individual-specific (random

Hello, all. I'm investigating the rate at which skeletal joint surfaces pass through a series of ordered stages (changes in morphology). Current statistical methods in this type of research use various logit or probit regression techniques (e.g., proportional odds logit/probit, forward/backward continuation ratio, or restricted/unrestricted cumulative probit). Data typically include the

? Hallo, which function can i use to do (baseline) logistic regression + goodness of fit tests? so far i found: # logistic on binary data lrm combined with resid(model,'gof') # logistic on binary data glm with no gof-test # baseline logit on binary data

Dear List, I have two questions about how to do predictions using lrm, specifically how to predict the ordinal response for each observation *individually*. I'm very new to cumulative odds models, so my apologies if my questions are too basic. I have a dataset with 4000 observations. Each observation consists of an ordinal outcome y (i.e., rating of a stimulus with four possible

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

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

Dear R-Help, I'm hoping to find a Multinomial Nested Logit package in R. It would be great to find something analogous to "PROC MDC" in SAS: > The MDC (Multinomial Discrete Choice) procedure analyzes models > where the > choice set consists of multiple alternatives. This procedure > supports conditional logit, > mixed logit, heteroscedastic extreme value,

I'm trying to obtain the robust standard errors for a multinomial ordered logit model: mod6 <- lrm(wdlshea ~ initdesch + concap + capasst + qualrat + terrain,data=full2) The model is fine but when I try to get the RSE I get an error. coeftest(mod6, vcov = vcovHAC(mod6)) Error in match.arg(type) : 'arg' should be one of “ordinary”, “score”, “score.binary”, “pearson”,

Dear all, I am facing some problem with how to fit a "Baseline category Logit model" with R. Basically I am considering famous "Alligator" data as discussed by Agresti. This data can also be found here: https://onlinecourses.science.psu.edu/stat504/node/174 (there is also an accompanying R file, however the underlying R code could not load the data properly!!!) Below are

I'm running a multinomial logit in R using the Zelig packages. According to str(trade962a), my dependent variable is a factor with three levels. When I run the multinomial logit I get an error message. However, when I run 'model=logit' it works fine. any ideas on whats wrong? ## MULTINOMIAL LOGIT anes96two <- zelig(trade962a ~ age962 + education962 + personal962 + economy962 +

I'm interested in doing multinomial logistic regression with equality constraints on some of the parameter values. For example, with categorical outcomes Y_1 (baseline), Y_2, and Y_3, and covariates X_1 and X_2, I might want to impose the equality constraint that \beta_{2,1} = \beta_{3,2} that is, that the effect of X_1 on the logit of Y_2 is the same as the effect of X_2 on the

Good morning, I'd like to estimate a simple multinomial logit model in R (not a McFadden conditional logit). For instance, I'd like to estimate the probability of someone having one of eight titles in a company with the independent variables being the company characteristics. A binary logit is well documented. What about the multinomial? Thanks, Walt Paczkowski

A multinomial logit model can be specified as a conditional logit model after restructuring the data. Doing so gives flexibility in imposing restrictions on the dependent variable. One application is to specify a loglinear model for square tables, e.g. quasi-symmetry or quasi-independence, as a multinomial logit model with covariates. Further details on this technique and examples with several

Hello I am having some problems for extracting the residuals from models working with ordinal multinomial data. Either working with the polr() function or the plsRglm () function, the residuals are "NULL". I guess this is because the data is multinomial but I do not know how to solve it. I have read the following in internet: "can you tell us how residuals would be defined in

Hi everyone? Does anybody know how can I estimate a Latent class multinomial (or conditional) logit using R? I have tried flexmix, poLCA, and they do not seem to support this model. thanks in advance adan -- View this message in context: http://r.789695.n4.nabble.com/Latent-class-multinomial-or-conditional-logit-using-R-tp4230083p4230083.html Sent from the R help mailing list archive at