similar to: Single Variable mlogit formatting

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

Hi: When I call str() on an mlogit object, I seem to get warnings. This code is from an example provided in the mlogit documentation: library(mlogit) data("Train", package="mlogit") tr<-mlogit.data(Train, shape="wide", choice="choice", varying=4:11, sep="", alt.levels=c(1,2), id="id")

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")

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) >

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

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",

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

To back up Ber's please have a look at http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example and/or http://adv-r.had.co.nz/Reproducibility.html John Kane Kingston ON Canada > -----Original Message----- > From: jose.ferraro at logiteng.com > Sent: Wed, 13 Apr 2016 17:18:35 +0000 > To: cdesjard at umn.edu > Subject: Re: [R] reduced set of

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,

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, 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'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

Hello, There's a typo in your call to mlogit.data, it should be alt.var="nbChev", not alt.var="noChev". Then the error is different. You should check the call arguments to see if they make sense. Hope this helps, Rui Barradas Em 28-07-2017 13:14, sandoz at free.fr escreveu: > I re post my question with the csv problem fixed. > > Can someone explain the

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,

Hi, I am trying to use mlogit in R to analyse a block design choice experiment dataset. The 12 choice sets/cards have been randomly assigned to 3 different blocks and each respondent answers all sets from one block (i.e. each respondent chooses from 4 choice sets). In total I have 2336 rows of data from 292 respondents. The data looks like this (2 of the three blocks): ID Sex Age Block Card

Hello, I am using the mlogit package to model diplomatic representation and stability. Here is our sample model - m2 = mlogit(y ~ 0|(time.curr + period), data=dip, alt.levels=c("asym","end","sym","x"), shape="wide", subset=which(mlogdata$curr=="sym")) >From here, we would need to find the probabilities and the standard error of

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

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 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

Hi After good advices i post this problem here again. Now the attached sambledata is in csv form and it is a part of my real data. I have tried different na.action operations but the result stays the same... My code > library(mlogit) > library(foreign) > z<-odbcConnectExcel("D:\\SAMBLEDATA.csv") > y<-sqlFetch(z,"Taul1") >