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What does logistic regression tell us?
Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.
What is the mixed logit model?
Mixed logit is a highly flexible model that can approximate any random utility model (McFadden and Train, 2000). It obviates the three limita- tions of standard logit by allowing for random taste variation, unre- stricted substitution patterns, and correlation in unobserved factors over time.
How to make a mixed effect multinomial logistic regression?
To fit a mixed effects multinomial logistic regression model, you would need to change your family from “binomial” to whatever the R package you are using suggests you should be using in a multinomial context. I am not familiar with this package, so others here may be able to give you hints on what the appropriate family to use would be.
What does multinomial mean in a mixed model?
On your left hand side of the model, you indicated through your response variable naming that your response is multinomial, which would mean that your response is NOT binary since it takes more than 2 values (i.e., at least 3). These values would be “categories” – for instance “failure”, “success”, “undetermined”.
Can you use multinomial mixed models with glmmadmb?
Use of this package for multinomial mixed models has already been published ( http://www.sciencedirect.com/science/article/pii/S0378112715007288 ). Thanks a lot in advance for your answers. Thanks for contributing an answer to Cross Validated!
Are there any multinomial multilevel mixed effect models?
STATA 13 recently added this feature to their multilevel mixed-effects models – so the technology to estimate such models seems to be available. Details: A number of research questions require the estimation of multinomial logistic regression models in which the outcome variable is categorical.