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Discrete
Choice
Discrete Choice is a package
for the fitting of a variety of models with categorical dependent variables.
These models are particularly useful for researchers in the social, behavioral,
and biomedical sciences, as well as economics, public choice, education,
and marketing.
Output for these models includes full information maximum likelihood estimates
with either standard and quasi-maximum likelihood inference. In addition,
estimates of marginal effects are computed either as partials of the probabilities
with respect to the means of the exogenous variables or optionally as the
average partials of the probabilities with respect to the exogenous variables.
Models
Nested logit model
- Is derived from the
assumption that residuals have a generalized extreme value distribution and
allows for a general pattern of dependence among the responses thus avoiding
the IIA problem, i.e., the "independence of irrelevant alternatives."
Conditional logit
model
- Includes both variables
that are attributes of the responses as well as, optionally, exogenous variables
that are properties of cases.
Multinomial logit
model
- Qualitative responses
are each modeled with a separate set of regression coefficients
Adjacent category multinomial
logit model
- The log-odds of one
category versus the next higher category is linear in the cutpoints and explanatory
variables
Stereotype multinomial
logit model
- The coefficients of
the regression in each category are linear functions of a reference regression
Poisson regression,
left or right truncated, left or right censored, or zero-inflated models
- Estimates model with Poisson
distributed dependent variable. This includes censored models - the dependent
variable is not observed but independent variables are available - and truncated
models where not even the independent variables are observed. Also, a zero-inflated
Poisson model can be estimated where the probability of the zero category
is a mixture of a Poisson consistent probability and an excess probability.
The mixture coefficient can be a function of independent variables.
Negative binomial regression,
left or right truncated, left or right censored, or zero-inflated models
- Estimates model with negative
binomial distributed dependent variable. This includes censored models -
the dependent variable is not observed but independent variables are available
- and truncated models where not even the independent variables are observed.
Also, a zero-inflated negative binomial model can be estimated where the probability
of the zero category is a mixture of a negative binomial consistent probability
and an excess probability. The mixture coefficient can be a function of independent
variables.
Logit, probit models
- Estimates dichotomous
dependent variable with either Normal or extreme value distributions
Ordered logit, probit
models
- Estimates model with
an ordered qualitative dependent variable with Normal or extreme value
distributions
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