Skip to main content

Questions tagged [multinomial-logit]

Multinomial logistic regression models a categorical dependent variable that can take on >2 different levels.

Filter by
Sorted by
Tagged with
2 votes
0 answers
23 views

Multimodal model and logistic regression

If I trained a model for a multinomial outcome but then later the outcome is grouped to be a binary outcome, can I just map the prediction of the multinomial model to the corresponding binary outcome ...
Stupid_boy's user avatar
1 vote
1 answer
34 views

A doubt for a multinomial mixed effect model

Suppose I had 243 individuals take a questionnaire. The questionnaire contains 100 questions referring to certain verbs, and the individual, for each verb, has to guess the noun or adjective that is ...
Giovanni Tinervia's user avatar
1 vote
1 answer
59 views

How to find the expected value of $p_k$ in multinomial regression

Given a multinomial regression the probability of a certain class $k$ is a function of predictors. How would we find analytically the expected value of $p_k$ given we know the distribution of ${\bf X}...
Vefeagins's user avatar
  • 610
0 votes
1 answer
29 views

Why my Rho-square on Multinomial Logit Model (McFadden) so small?

When I'm using MNL, and try to find my rho square, it's found out to be so small. It is $0.0139$. For a good fit model, the rho square has to be between $0.2$-$0.4$. Is there any reason why it's so ...
Fajri's user avatar
  • 1
2 votes
0 answers
41 views

Can I use Multinomial Logistic Regression's probabilities as proxies for proportions?

Summary of the Problem: I am exploring methods to simultaneously predict the proportions of different tree species within 250 square meter forest plots using ALS (Airborne Laser Scanning) and spectral ...
candelas762's user avatar
2 votes
1 answer
99 views

How to manually calculate marginal effects from output of a multinomial logistic regression

I need to calculate marginal effects based on coefficients from a mutlinomial logistic regression Here is some toy R code (apologies users of other software but concepts translate across platforms so ...
llewmills's user avatar
  • 2,063
1 vote
1 answer
80 views

Multinomial logistic regression vs multiple binary logistic regression [duplicate]

I was practising codes from the textbook, "Discovering Statistics using R". I wonder if running two binary logistic regression can be another way of running one multinomial logistic ...
Junho65's user avatar
  • 13
0 votes
0 answers
16 views

Mixed Logit - Identification of beta/sigma

Since MXL is invariant to scale (utility is ordinal), we only identify $\beta/\sigma$, not $\beta$ or $\sigma$ independently. But is it possible to use the estimated $\hat{\sigma}$ from the regression ...
Adam Samson's user avatar
0 votes
0 answers
23 views

What type of statistical analysis is recommended for examining variations in risk and protective factors across male and female offending groups

I want to understand how risk and protective factors vary across separate male and female offending groups I have classified them into using group-based trajectory modelling (GBTM). Using GBTM, I ...
Ayda's user avatar
  • 1
1 vote
0 answers
27 views

Multinomial Logit Extension

The derivation of the multinomial logit probabilities depends on the difference of two Type 1 extreme value (Gumbel) random variables following a logistic distribution. We say the unobserved utility ...
Adarsh Nayak's user avatar
4 votes
1 answer
261 views

Detailed derivation for the log likelihood of a logistic multinomial model

I am working with some Bayesian model development involving the logistic-normal multinomial model. However, I am quite confused about how to express the multinomial part. Traditionally, the full ...
Mangnier Loïc's user avatar
0 votes
1 answer
83 views

Regression for Likert scale SPSS

I have taken a survey. Dependent variable is on Likert scale (1-5). Independent variables are also on Likert scale 1-5. What kind of regression should i better use? I thought about multinomial (or ...
TylerJ's user avatar
  • 1
2 votes
1 answer
182 views

Are ordinal models technically a GLM?

I'm following Agresti's book. Specifically, chapter 6 which deals with multinomial models. In that he shows how a nominal response can be modeled by a baseline category GLM, and shows it can be viewed ...
Maverick Meerkat's user avatar
3 votes
1 answer
61 views

Why run a chi-square of independence test before the multinomial logistic regression?

I would like to find out why is it necessary and key to run the chi-square test before the multinomial logistic regression? I am reading someone's unpublished work and it says the Chi square test is ...
Amelia Nicodemus's user avatar
0 votes
1 answer
61 views

Identical intercept and random effect values in multinomial regression model in R

I have 6 animals whose behaviour I’m testing over multiple trials. It’s a pretty small dataset (max 15 observations per animal). Behaviour falls into multiple types. The same behaviour can occur ...
anon's user avatar
  • 3
0 votes
0 answers
11 views

Ensuring numerical stability of probabilities in identifiable multinomial regression

I'm looking to understand the differences between the functions for probabilities induced in an identified multinomial GLM, and the softmax function commonly used in classification for neural networks....
bill's user avatar
  • 1
0 votes
0 answers
50 views

Are these glmer and mblogit models equivalent?

I'm trying to eventually fit a multinomial mixed effects model. It seems mclogit::mblogit can do this, so I'm trying to compare it with ...
Spacedman's user avatar
  • 1,592
1 vote
1 answer
38 views

Getting a very big relative risk ratio value

We have fitted a multilevel multinomial logistic regression model to our data. We have obtained relative risk ratios(RRR). For most of the independent variables RRR have usual values, like 0.49, 0.78, ...
user232597's user avatar
1 vote
0 answers
60 views

How many lags of independent variables to use

I have a panel dataset (26880 observations) of individual decisions ($y_{it}$, a categorical variable) which depends on signals ($x_{it}$). I am trying to find out how many past signals individuals ...
jasmine's user avatar
  • 357
0 votes
0 answers
52 views

What model could be the best for this problem?

I would like to know which model could be the best for the problem i have: I have data at baseline, at 3 months Follow-up, and data until 2 years of follow up. I have classified individuals as if they ...
BPeif's user avatar
  • 143
0 votes
0 answers
27 views

Logistic regression after a 3 year follow up where successful people can leave the sample

I have data tracking the outcomes of young people participating in an intervention project (between the ages of 12-16). In 2016, data was taken of all young people who had joined the project that year....
Luke Jenner's user avatar
1 vote
0 answers
24 views

Multinomial regression for modelling change of proportional makeup

I’m reaching the limits of my statistical understanding here when it comes to model specification in BRMS. I’m a PhD student researching how different drivers of species distribution (generalised into ...
Dan341's user avatar
  • 11
3 votes
1 answer
77 views

What standard error should I use with correlated clusters in maximum likelihood estimation of multinomial logit

I have a dataset with 14 clusters. Each cluster is a time series of 80 periods with autocorrealtion, and I am doing maximum likelihood estimation of a structural multinomial logit model. I suspect ...
jasmine's user avatar
  • 357
1 vote
1 answer
85 views

Calculate (quasi) AIC for mixed-effect baseline-category (multinomial logit) model

I am doing a discrete choice experiment where respondents are presented with different patient profiles, and for each profile, respondents need to choose one (out of three) treatment options. An ...
Trang Hien's user avatar
0 votes
0 answers
14 views

Study changes of association between an independent variable and a categorical outcome with more than 2 categories

Suppose I would like to study the association between sex (male and female; independent variable) and taste preference (sweet, bitter, and sour; dependent variable). I have three cross-sectional ...
Guoqiang Zhang's user avatar
0 votes
0 answers
27 views

Assessing differences in effect size of an independent variable: multinomial logistic regression or one-vs-rest binary logistic regression

My question pertains to this Cross Validated post, but I specifically aim to compare the effect size of an independent variable between categories of the dependent variable. In practice, I have a non-...
Abel Aussant's user avatar
3 votes
1 answer
57 views

Multinomial logit models with nested categories in DV

I've wanted to ask for some help with multinomial logit models. I'm investigating how the prior probability of song lyrics affects what lyrics we actually hear. In my experiment there are 30 songs (we ...
Moshe Poliak's user avatar
0 votes
2 answers
122 views

Reporting multinomial regression results to a non-stat audience

I would appreciate a little bit of advice on reporting. I've run a set of multinomial regression models, done my model testing, all that good stuff. Now I am waffling on how I want to report the ...
a_t_rex's user avatar
0 votes
0 answers
24 views

"decreased", "same", "increased" outcomes : ordinal logistic vs multinomial logistic?

I am conducting a study where I have ordinal categorical outcome variables representing frequency for two distinct periods. My goal is to consolidate the responses from these periods into a single ...
HYL's user avatar
  • 377
0 votes
1 answer
220 views

Multinom package in r , but some participants with multiple observations

I am conducting a multinomial regression using multinom package in r. Outcome is the day the week a patient was discharged (7 levels) and there are a few predictors that are not choice-specific (age, ...
Hassan's user avatar
  • 1
0 votes
0 answers
20 views

Multinomial regression with duplicated data points: aggregation or mixed effects?

I would like to run a multinomial model, and I would like feedback on how to deal with duplicates. Suppose the following example, which is quite close to my actual research question. I have data on a ...
M. Riera's user avatar
2 votes
1 answer
284 views

mlogit + logitr packages fail to recover true estimates of mixed logit random coefficient model

I am running Monte-Carlo simulations on a simple DGP of a mixed logit random coefficient model to check if the mlogit and logitr ...
JediKnight's user avatar
4 votes
1 answer
193 views

How robust is multinomial logistic regression to having a multi-label problem shoehorned into it?

Consider a situation where there can be membership in group $A$, group $B$, both groups, or neither group. If we wanted to predict group membership probabilities from some covariate information, this ...
Dave's user avatar
  • 65.6k
2 votes
0 answers
341 views

multinom() clustered standard error [closed]

I am attempting to identify the appropriate code for calculating clustered standard errors following the execution of a regression using the multinom() function. I attempted the following code, but ...
NicRcodes's user avatar
2 votes
1 answer
72 views

How to address dependent observations in a multinomial logistic regression model

I'm studying the influence of fund size and board diversity on the voting behavior of pension funds (in terms of voting on shareholder resolutions at companies they invest in). My dependent variable, '...
Magdalena's user avatar
1 vote
0 answers
15 views

What is a procedure for selecting a type of logistic regression for predicting one of 5 classes based on 3 continuous predictors?

What is a procedure for selecting a type of logistic regression model (e.g., multinomial, one versus rest) for predicting the class of a pixel given its red, green, and blue intensities as in https://...
Tom Lever's user avatar
  • 121
1 vote
0 answers
374 views

Statsmodel multinomial logistic regression outputs all nan values [closed]

I'm trying to fit a multinomial logreg with statsmodel. import statsmodel as sm X = sm.add_constant(df) logreg_model = sm.MNLogit(y[:n], X).fit() Where df is a one ...
pedritoanonimo's user avatar
1 vote
1 answer
52 views

Drawing numbers using the CDF

Say I have a (generally high-dimensional) random variable $X$ with known, continuous CDF $F(X)$. Is there a good algorithm for drawing values of $X$ that doesn't require that I calculate the joint ...
Wilbur's user avatar
  • 211
0 votes
1 answer
54 views

Choosing between multinomial logistic regression or binary logistic regression for interchangeable variables

I want to estimate how likely a disease is associated with symptom (dummy hypothesis). Say that I want to assess which of avian flu, swine flu, and common flu is more commonly associated with fever. ...
amedicalenthusiast's user avatar
1 vote
0 answers
135 views

How to model time-varying probability in Markov process with multinomial logistic regression?

I have multiple discrete time Markov processes. They each consist of the same 12 categorical states. I want to model how the probability of each of these states varies over time across each process. ...
Captain Ahab's user avatar
0 votes
1 answer
104 views

Converting a multinomial logit model into a binary logit model

As far as I remember, it is possible to convert a multinomial logit model into a binary logit model using restrictions on parameters. For example, suppose we have three alternatives, say A, B, and C. ...
MinChul Park's user avatar
2 votes
0 answers
35 views

What kind of regression for predicting limited resource allocation across items

I have data where people are given a limited number of tokens (1 - 10) which they can assign across five different items. The number of tokens they allocate to an item represents the importance of ...
Simon Myers's user avatar
3 votes
0 answers
131 views

Is multinomial logistic regression symmetric?

Simple linear regression is symmetric in the sense that, if I regress $Y$ on $X$ or $X$ on $Y$, I get the same $R^2$ and result from the overall $F$-test. ...
Dave's user avatar
  • 65.6k
0 votes
1 answer
1k views

Plotting probabilities from multinomial regression output

I generated some data to visualize a multinomial logistic regression, where individuals choose a mode of transportation based on their income. I then set up a regression and predicted the ...
Sebastian Geis's user avatar
0 votes
0 answers
35 views

interpreting multinomial logistics regression coefficients into odds

I am using sklearn logistic regression to predict age (numeric) vs gender (male, female, non-binary) - the below are intercept [ 18.5455613 -1.83610814 -14.10055903] these are the coefficients of ...
pranav nerurkar's user avatar
1 vote
1 answer
86 views

Multinomial logit: why likelihood for one observation uses probabilities of all classes?

When dealing with non-binary discrete-choice outcomes, one common way of modeling such problems as a function of some covariates is through a multinomial logit/logistic model, in which there is one ...
anymous.asker's user avatar
3 votes
0 answers
20 views

How to identify individuals that don't belong to a training class?

The frequency of 8 cell types is measured in 100 patients (the frequencies do not sum up to 1). The patients form 4 pathologies established by the physicians. As there might be better markers (cell ...
SamGG's user avatar
  • 51
2 votes
1 answer
59 views

Multinomial/Categorical models with a variable number of outcomes

Is it possible to implement a multinomial/categorical model where the number of categories itself is variable? For example, say I have two surveys with the following questions/responses: What ice ...
Mark Rieke's user avatar
0 votes
1 answer
683 views

How to run a multinomial logistic regression with mixed effects in R?

I'm trying to run a multinomial logistic regression with mixed effects. Let's say I have the following variables: Participant (ten participants, each with 10 observations) Word (ten different words, ...
user avatar
2 votes
0 answers
94 views

How should I impute a 100% missing element of a composite variable?

I want to model the variable $Y$ (score 1 to 10) that is a composite of 5 ordinal variables $Y_{a:e}$ with the same levels (0 = "not at all", 1 = "a little bit", 2="a lot"...
usual_user16960220's user avatar

1
2 3 4 5
7