similar to: Output mlogit package (multinomial logistic regression)?

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 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

Hi all, Does anyone knows how to handle ordered preferences applying the R package mlogit (multinomial logit model)? My data set provides for each customer preferences (given as percentages) for 6 different brands. I would like to use for model calibration not just that brand with maximum stated preference. I know that ordered preferences can be used in the R package MNP (multinomial Probit

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]]

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

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 =

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) >

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: 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

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

Dear all, does anybody have experience with building logits in Mlogit? I want to test the use of a couple of alternative specific variables with a generic regression coefficient. However, one of them simply does not work. R says the length of this variable is different. Problem: If I check the length of this special variable, I get a value, which also other variables have ? and with those, the

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",

-----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.

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

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, 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

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

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

Dear R-group, Could somebody recommend a package that can deal with a multinomial response variable (choice of breeding tactic in mice, which has four unordered levels), multiply-imputed data (generated using the Amelia package) and two non-nested random effects: individual identity (133 individuals made up to four choices each) and year (for which there are six levels and sample size varies