Stack Overflow
Stack Overflow

Questions tagged [mlogit]

Multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems.

Multinomial Logistic Regression

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).

Multinomial logistic regression is known by a variety of other names, including multiclass LR, multinomial regression, softmax regression, multinomial logit, maximum entropy (MaxEnt) classifier, conditional maximum entropy model.

Source: http://en.wikipedia.org/wiki/Multinomial_logistic_regression

289 questions
2
votes
0 answers

Choice data with no-choice option in mlogit

I struggle with modeling a no-choice option within the mlogit package to estimate part worths from conjoint data. I have choice data from 600 respondents (respid). Each respondent choose between 3 hypothetical products (alt: A-C) and a no choice…
Michael
  • 41
  • 2
2
votes
0 answers

Varying choice sets, large data, mnlogit

I am trying to estimate a multinomial logit model of customer store choice. There are different choice sets for each customer. The dataset is large, so mlogit take too long to converge. mnlogit is faster with large data sets, but I can't figure out…
gannawag
  • 225
  • 2
  • 12
2
votes
0 answers

Mixed-logit model in R

I was trying to estimate the mixed-effects logit in R using mlogit package. I would be grateful if someone could point out where is my mistake. My original data has a panel structure - each individual makes choice in 6 different scenarios. Each time…
Fujifilm
  • 21
  • 1
  • 3
2
votes
1 answer

multinomial/conditional logit with grouped data in R

I'm looking for a way to fit a conditional logit on grouped data in R. I have multinomial choice counts with response- and individual-specific variables. Unfortunately, the expanded dataset would be too huge to work with it. Thus I can't use most of…
Cabana
  • 419
  • 2
  • 7
2
votes
0 answers

Python - Overflow issues with conditional logit model

This is my first post on stack overflow so please be indulgent! I am trying to develop a code for a multinomial (or conditional) logit model (as described in http://data.princeton.edu/wws509/notes/c6s3.html) This type of model can be seen as an…
Umka
  • 45
  • 6
2
votes
1 answer

Generating confidence intervals for predicted probabilities after running mlogit function in R

I have been struggling with the following problem for some time and would be very grateful for any help. I am running a logit model in R using the mlogit function and am able to generate the predicted probability of choosing each alternative for a…
James
  • 35
  • 4
2
votes
0 answers

R mlogit-Error system is computationally singular: reciprocal condition number

I am using library(mlogit) in R and I am stuck at this particular data set which looks like this CustomerID Item Price Calories Choice 1 200 1.99 490 NO 1 312 4.99 887 NO 1 560 5.19 910 …
AgentSmith
  • 21
  • 4
2
votes
0 answers

Error in : object of type 'closure' is not subsettable (2)

I know this question has been raised before (Error in : object of type 'closure' is not subsettable). But I could not get my head around it. Here is the packages I use and how I prepare my data library(mlogit) data(CollegeDistance,…
Milad
  • 339
  • 2
  • 12
2
votes
0 answers

R nnet multiniom (multinomial logistic regression models) - assign penalties to avoid misclassification

I am using multinom from nnet package to fit a logistic regression model to data consists of 3 classes, however the prevalence of the classes is not balanced. I would like to assign weight/penalties in order to tell the model to avoid…
user3628777
  • 529
  • 3
  • 10
  • 20
2
votes
1 answer

R mlogit on my data giving error 'system is computationally singular?

Could anyone explains what is the error and how could I resolve it? > C.m<-mlogit(choice~TotalAmount,data=C) Error in solve.default(H, g[!fixed]) : system is computationally singular: reciprocal condition number = 6.87101e-23
chee.work.stuff
  • 326
  • 2
  • 14
1
vote
1 answer

Issue with varying in multinomial mixed logit model using mlogit

I'm trying to run a random intercept multinomial logit model ID Resolution STATE Index_1 Index_2 6 Settled Indiana 43.3267816 49.2 8 Settled Delaware 72.6980536 48.5 9 Dismissed Delaware 72.6980536 48.5 10 Settled New…
mmm coder
  • 11
  • 1
1
vote
0 answers

Error no individual index in mixed logit model using mlogit package in R

part of data set I am estimating a mixed logit model with panel data from a stated choice experiment in R. I tried several things, but I get an error that I do not have an individual index. So for your information, each respondents got 8 choice…
Marise Zuurbier
  • 11
  • 2
1
vote
0 answers

MultinomialLogit() with xlogit - ValueError: inconsistent 'y' values. Make sure the data has one choice per sample

I'm learning about multinomial logit and I have a problem running this code using xlogit. # Long format from xlogit.utils import wide_to_long ATUS_data_LA_2020_Long = wide_to_long(ATUS_data_LA_2020_wide, id_col='custom_id', alt_name='alt', sep='_', …
Catalina V
  • 13
  • 3
1
vote
1 answer

choice experiment data: mlogit exercise 3 "error in reshapelong.... 'varying arguments must be same length'

Following Exercise 3 of the mlogit package https://cran.r-project.org/web/packages/mlogit/vignettes/e3mxlogit.html, but attempting to use my own data (see below) structure(list(Choice.Set = c(4L, 5L, 7L, 8L, 10L, 12L), Alternative = c(2L, 1L, 1L,…
Chris Bova
  • 134
  • 1
  • 2
  • 9
1
vote
0 answers

Multinomial mixed effects model R

I want to run a multinomial mixed effects model with the mclogit package of R. Below can be show the head of my data frame. > head(mydata) ID VAR1 X1 Time Y other_X3 other_X4 other_X5 other_X6 other_X7 1 1 1 1 1 10 0 0 …
Didie
  • 31
  • 3
1 2 3
…
19 20