similar to: mlogit

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 learning five mlogits as follows v1.model<-mlogit(v1~1|v2+v3+v4+v5, data=mlogit.v1.data, reflevel="1") v2.model<-mlogit(v2~1|v1+v3+v4+v5, data=mlogit.v2.data, reflevel="1") v3.model<-mlogit(v3~1|v1+v2+v4+v5, data=mlogit.v3.data, reflevel="1") v4.model<-mlogit(v4~1|v1+v2+v3+v5, data=mlogit.v4.data, reflevel="1")

Hi all, I have been working through the examples in one of the vignettes associated with the 'mlogit' package, 'Kenneth Train's exercises using the mlogit package for R.' In spite of using the code unchanged, as well as the data used in the examples, I have been unable to run a Wald test to test two models. Specifically, I have run the following command, where mc and mi2 are

I am new to R but I have managed to use mlogit to run multivariate logit models successfully. My data violates the Independence of Irrelevant Alternatives assumption and now I would like to run a mixed logit model. It is a "wide" data set with 9 independent (individual) variables and three choices (variable Y). The database is in a cvs file called CAU. This is the code I have run

I am using the mlogit packages and get a data problem, for which I can't find any clue from R archive. code below shows my related code all the way to the error #--------------------------------------------------------------------------- mydata <- data.frame(dependent,x,y,z) mydata$dependent<-as.factor(mydata$dependent) mldata<-mlogit.data(mydata, varying=NULL,

I'm trying to fit a multinominal logistic model using package mlogit. I have 15 independent variables. The code looks like this: m<-mlogit(score~0|f1+f2+f3+f4+f5+f6+f7+f8+f9+f10+f11+f12+f13+f14+f15, data, reflevel="1") And it gives the following error message: Error in parse(text = x) : unexpected ')' in "score ~ 0 + alt:(f1 + f2 + f3 + f4 + f5 + f6 + f7 + f8 + f9

Hi all - my first time here and am having an issue with the Predict function. I am using a tutorial as a guide, locate here: http://www.ats.ucla.edu/STAT/R/dae/mlogit.htm My code gives this error > newdata1$predicted <- predict(mlogit,newdata=newdata1,type="response") Error in `$<-.data.frame`(`*tmp*`, "predicted", value = c(0.332822934960197, : replacement has

Hello. I am beginning to analyze my work and have realized that a simple chi-square analysis will not suffice for my research, with one notable reason is that data are not discrete.  Since my data fit the assumptions of a logistic regression, I am moving forward with this analysis.  With that said, I am a beginner with R and would grealty appreciate any help!  Essentially, the point of my work

Hello everyone, I am using *mlogit* to analyse my choice experiment data. I have *3 alternatives* for each individual and for each individual I have *9 questions*. I have a response from *516 individuals*. So it is a panel of 9*516 observations. I have arranged the data in long format (it contains 100 columns indicating different variables and identifiers). In mlogit I tried the following

Hello, I've been getting the following error when using the mlogit function from the mlogit package This is one of the examples provided in the Package "mlogit" January 27, 2010 description data("Fishing", package="mlogit") Fish <- mlogit.data(Fishing, varying = c(4:11), shape="wide", choice="mode") summary(mlogit(mode ~ pr + ca - 1,

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 =

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

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

Hello, I can't figure out why using and not using weights in mlogit yields identical results. My motivation is for the case when an "observation" or "individual" represents a number of individuals. For example, library(mlogit) library(AER) data("TravelMode", package = "AER") TM <- mlogit.data(TravelMode, choice = "choice", shape =

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 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 am trying to perform a backwards stepwise variable selection with an mlogit model. The usual functions, step(), drop1(), and dropterm() do not work for mlogit models. Update() works but I am only able to use it manually, i.e. I have to type in each variable I wish to remove by hand on a separate line. My goal is to write some code that will systematically remove a certain set of variables

Hello,  I am pretty new to mlogit, and still trying to figure out what models to use.I have a data set of N individuals, each of which faces I alternatives. The utility function of individual n, for choice i is:  u(i,n) = alpha(i) * x1(i,n) + beta * x2(i,n)  where alpha(i) is the individual specific parameter, and beta is shared among all individuals. I don't really know how to set this up

I am trying to estimate multinomial logit models off of a .csv table in IDCASE IDALT format where I have ROWS HHID PERID CASE ALTNUM NUMALTS CHOSEN IVTT OVTT TVTT COST DIST WKZONE HMZONE RSPOPDEN RSEMPDEN WKPOPDEN.... 1 1 2 1 1 1 5 1 13.38 2.00 15.38 70.63 7.69 664 726 15.52 9.96 37.26 2 2 2 1 1 2 5 0 18.38 2.00

Hi, Please help about the error I am getting after the h1.dat<- line : this line worked with much more independant variables and bigger data. This time I want to work with just 2 variables cteD & cteTh. What is wrong ? > setwd("C:/Rstudio/Trot") > library(mlogit) > horse1.data<-read.csv("cte2.csv") >