similar to: multinomial choice modeling with mlogit

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,

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, 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

I have been trying to use the mlogit package to do a multinomial logistic regression, including both alternative-specific and individual-specific variables. I used the mlogit.data function to turn my dataframe into the correct format for the mlogit function, and have been able to run the regression. However, I would like to weight the different cases differently. (Just to clarify, it's not the

Dear all, ? I am implementing a stochastic utility model that will eventually make use of multinomial logit. I found that there is a package in R called mlogit. I am not sure whether I have already found the correct package or software. May I ask am I correct? ? Basically, let's say ? I have observations of n outcomes, for each outcome 1<=i<=n, they were selected by a choice from a set

We would like to announce the release of our software, which is now available through CRAN. MNP: R Package for Fitting the Multinomial Probit Models Abstract: MNP is a publicly available R package that fits the Bayesian multinomial probit models via Markov chain Monte Carlo. Along with the standard multinomial probit model, it can also fit models with different choice sets for each observation,

We would like to announce the release of our software, which is now available through CRAN. MNP: R Package for Fitting the Multinomial Probit Models Abstract: MNP is a publicly available R package that fits the Bayesian multinomial probit models via Markov chain Monte Carlo. Along with the standard multinomial probit model, it can also fit models with different choice sets for each observation,

Hi All, Can the MNP package available in R be used to analyze panel data as well? *i.e., *if there are 3 observed discrete choices for three time periods for the same individual , can i estimate a panel multinomial probit model which allows correlated errors across time periods and individual heterogeneity (random coefficients) using the MNP package? In the case that it doesn't work, is

Hello, For my master thesis I conducted a conjoint analysis. Using the mlogit-package of Yves Croissant I should be able to analyse my choice data. I read the whole program of mlogit, but I do not understand how my coefficients must be interpreted. Are they a result of P(Y=i) or are they P(Y=i)/P(Y=1) where 1 is my base category, or are they a result of ln(P(Y=i)/P(Y=1)). I can not find an answer

Hi all, Does anybody have a hint on what may be going wrong in this R code? I mimic the sample code from the MNP developers but I seem unable to get the choice specific variables right. Thanks, Joan Serra > rm(list=ls()) > library(foreign) > small<-read.spss("small.sav") Warning message: small.sav: Unrecognized record type 7, subtype 13 encountered in system file. >

I want to analyse data with an unordered, multi-level outcome variable, y. I am asking for the appropriate method (or R procedure) to use for this analysis. > N <- 500 > set.seed(1234) > data0 <- data.frame(y = as.factor(sample(LETTERS[1:3], N, repl = T, + prob = c(10, 12, 14))), x1 = sample(1:7, N, repl = T, prob = c(8, + 8, 9, 15, 9, 9, 8)), x2 = sample(1:7, N, repl =

Hi All, I am attempting to build a Multinomial Logit model with dummy variables of the following form: Dependent Variable : 0-8 Discrete Choices Dummy Variable 1: 965 dummy varsghpow at student.monash.edu.augh@gp1.com Dummy Variable 2: 805 dummy vars The data set I am using has the dummy columns pre-created, so it's a table of 72,381 rows and 1770 columns. The first 965 columns represent

I am trying to follow this example for multinomial logistic regression http://www.ats.ucla.edu/stat/r/dae/mlogit.htm However, I cannot get it to work properly. This is the output I get, and I get an error when I try to use the mlogit function. Any ideas as to why this happens? > mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/mlogit.csv")) > attach(mydata) >

Dear All I am trying to fit a multinomial logistic regression to a data set with a size of 94279 by 14 entries. The data frame has one "sample" column which is the categorical variable, and the number of different categories is 9. The size of the data set (as a csv file) is less than 10 MB. I tried to fit a multinomial logistic regression, either using vglm() from the VGAM package or

Hello: Has anyone tried to model-based recursive partition (using mob from package party; thanks Achim and colleagues) a data set based on a multinomial logit model (using mlogit from package mlogit; thanks Yves)? I attempted to do so, but there are at least two reasons why I could not. First, in mob I am not quite sure that a model of class StatModel exists for mlogit models. Second, as

Sir, I want to fit a multinomial logistic regression in R.I think mlogit() is the function for doing this. mlogit () is in packege globaltest.But, I can not install this package. I use the following: install.packages("globaltest") Can you help me? Regards, Suman Dhara [[alternative HTML version deleted]]

-----Original Message----- From: Bert Gunter [mailto:bgunter.4567 at gmail.com] Sent: quinta-feira, 31 de mar?o de 2016 20:22 To: Jose Marcos Ferraro <jose.ferraro at LOGITeng.com> Cc: r-help at r-project.org Subject: Re: [R] reduced set of alternatives in package mlogit code? example data? We can only guess based on your vague post. "PLEASE do read the posting guide

Hi Jose, You're referring to your response variable when you're saying it's missing some of the choices, right? Are your response choices ever known or do they just occur with extremely low frequency? Either way, I think the mlogit package would be inappropriate for you. I imagine you would have much better luck using MCMCpack or writing a model with rstan or something Bayesian.

I've been working on a multinomial logit model, trying to predict vegetation types as a function of total phosphorus. Previous responses to my postings have pointed me to the mlogit package. I'm now trying to work examples and my data using this package. data("Fishing", package = "mlogit") Fish <- mlogit.data(Fishing, varying = c(4:11), shape = "wide",

Hi, I'm trying to fit a multinomial logistic regression to my data which consists of 5 discrete variables (scales 1:10) and 1000 observations. I get the following error: Error in `row.names<-.data.frame`(`*tmp*`, value = c("NA.NA", "NA.NA", : duplicate 'row.names' are not allowed In addition: Warning message: non-unique value when setting