similar to: ungrouped data for multinomial regression

Full_Name: José Otero Version: Version 1.5.0 (2002-04-29) OS: Redhat 7.3 Submission from: (NULL) (192.187.16.164) Hi: Installation of package faraway as root, from tarbal: R CMD INSTALL ./faraway.tar.gz ERROR: cannot extract package from './faraway.tar.gz' idem, from zipped package: R CMD INSTALL faraway.zip gzip: faraway.zip has more than one entry--rest ignored ERROR: cannot

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 Prof. Thomas Yee I$B!G(Bm very interested in your R program VGAM. I tried below your data: # Nonparametric proportional odds model data(pneumo)pneumo = transform(pneumo, let=log(exposure.time))vgam(cbind(normal,mild,severe) ~ s(let), cumulative(par=TRUE), pneumo) However, the results by Version of VGAM are different; ----------The result by Version 0.7-7

Hi, I have my data the following way: y A B C 0 1 1 2 0 1 2 1 1 1 1 2 0 1 1 2 1 1 1 2 1 1 2 1 0 1 2 2 . . . And so on. How can I make my data look like the following: y A B C 2 1 1 2 1 1 2 1 0 1 2 2 . . . In other words how can I change my ungrouped cases into grouped cases? Thanks! Chris [[alternative

I'm hoping the community knowledge can help me out here. I have found great value in R, especially using it to generate charts, but I am still scaling the learning curve in a number of ways. I am looking plot one grouped line and one ungrouped line in a lattice plot. I can plot one grouped line like this (the line's color in each panel becomes dependent on the newpool value):

Thanks for your answers Stephen and Ben, I hope I am posting on the correct list now. I managed so far to run the multinomial model with random effect with the following command: MCMCglmm(fixed=cbind(Apsy,Mygl,Crle,Crru,Miag,empty) ~ habitat:trait,random=~idh(trait):mesh,family="multinomial12", data=dataA,rcov=~trait:units) (where multiple responses are different species, Habitat

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,

Hi, Consider the need for a regression model which can handle an ordered multinomial response variable. There are, for example, proportional odds / cumulative logit models, but actually the regression should include random effects (a mixed model), and I would not be aware of multinomial regression model as part of lme4 (am I wrong here ?). Further, the constraint of proportional odd models

Dear all, Does someone know an R function implementing the method of Sison and Glaz (1995) (see full ref below) for computing Simultaneous confidence intervals for multinomial proportions? As alternative method, I think to boostrap the mean of each proportion and get in that way confidence interval of the mean. I observed 21 times a response that could be one out of 8 categories

Hi, i'm an italian student that use for the first time the mailing list. I need an help to use a function for elaborate a multinomial logit analisys. I'm making a paper on the USA's commons and their way of organize their service. The various tipologies of service are 4: public, contracting in, contracting out and no-profit. I have 10 different service for each common, and for each

Dear All, I am looking at discriminating among several individuals based on a few variable sets (I think some variables do not make sense unless they are entered together, so I "force" them into the models together, hence datasets). I have done so with linear discriminant analysis (LDA) using "MASS::lda", with acceptable results. However, one of my collaborators

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

I have two questions: 1. If I want to do a binomial logit, how to handle the categorical response variable? Data for the response variables are not numerical, but text. 2. What if I want to do a multinomial logit, still with categorical response variable? The variable has 5 non-numerical response levels, I have to do it with a multinomial logit. Any input is highly appreciated! Thanks! Ed

Hi Could you perhaps possibly help me. I would like to use the package VIF but cannot get results I attach the .csv file and my R code. What do I have to do ? Any help is greatly appreciated. library(VIF) coal <- read.csv("e:/freekvif/cqa1.csv",header=TRUE) y <- as.numeric(coal$AI) x <- as.matrix(cbind(coal$Gyp, coal$Pyrite, coal$Sid, coal$Calcite, coal$Dol,

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 __________________________________

I tried to use the effect() to get predicted marginals for multinomial logistic as I did for general logistic regression, but failed. Is there anyway to do that? Thx! -- View this message in context: http://www.nabble.com/How-to-get-predicted-marginal-%28aka-predicted-mean%29-after-multinomial-logistic--tp17200114p17200114.html Sent from the R help mailing list archive at Nabble.com.

I have had plenty of succes generating one dimensional variables and plotting them, but what do i do for more (specifically 2) dimensional multinomial variables? I figure i have to create a vector consisting of two 1 dim normallly distributed variables, that way i can also control the correlation. But how do i do this :) ? Many thanks -- View this message in context:

I want to get the logLik to calculate McFadden.R2 ,ML.R2 and Cragg.Uhler.R2, but the value from multinom does not have logLik.So my quetion is : is logLik meaningful to multinomial logistic model from multinom?If it does, how can I get it? Thank you! ps: I konw VGAM has function to get the multinomial logistic model with logLik, but I prefer use the function from "official" R

I have to the treatment effect base on the observational data.And the treatment variable is multinomial rather than binary.Because the treatment assignment is not random,so the selection-bias exists.Under this condition,what's the best way to estimate the treatment effect? I know that if the treatment is binary,I can use propensity score matching using MatchIt package.But what about

On May 24, 2011; 11:06pm Belle wrote: > Does anyone know how to run Multinomial logistical Model in R in order to > get predicted probability? Yes. I could stop there but you shouldn't. The author of the package provides plenty of examples (and two good vignettes) showing you how to do this. Suggest you do some work in that area. Look especially at how model formulas are