3.2.2. statsmodels.discrete.discrete_model

Limited dependent variable and qualitative variables.

Includes binary outcomes, count data, (ordered) ordinal data and limited dependent variables.

3.2.2.1. General References

A.C. Cameron and P.K. Trivedi. Regression Analysis of Count Data.
Cambridge, 1998
G.S. Madalla. Limited-Dependent and Qualitative Variables in Econometrics.
Cambridge, 1983.
  1. Greene. Econometric Analysis. Prentice Hall, 5th. edition. 2003.

3.2.2.2. Functions

approx_fprime(x, f[, epsilon, args, kwargs, ...]) Gradient of function, or Jacobian if function f returns 1d array
approx_fprime_cs(x, f[, epsilon, args, kwargs]) Calculate gradient or Jacobian with complex step derivative approximation
approx_hess(x, f[, epsilon, args, kwargs]) Calculate Hessian with finite difference derivative approximation
approx_hess_cs(x, f[, epsilon, args, kwargs]) Calculate Hessian with complex-step derivative approximation
fit_l1_slsqp(f, score, start_params, args, ...) Solve the l1 regularized problem using scipy.optimize.fmin_slsqp().
handle_data(endog, exog[, missing, hasconst])

3.2.2.3. Classes

BinaryModel(endog, exog, **kwargs)
BinaryResults(model, mlefit[, cov_type, ...]) A results class for binary data
BinaryResultsWrapper(results)
CountModel(endog, exog[, offset, exposure, ...])
CountResults(model, mlefit[, cov_type, ...]) A results class for count data
CountResultsWrapper(results)
DiscreteModel(endog, exog, **kwargs) Abstract class for discrete choice models.
DiscreteResults(model, mlefit[, cov_type, ...]) A results class for the discrete dependent variable models.
L1BinaryResults(model, bnryfit) Results instance for binary data fit by l1 regularization
L1BinaryResultsWrapper(results)
L1CountResults(model, cntfit) A results class for count data fit by l1 regularization
L1CountResultsWrapper(results)
L1MultinomialResults(model, mlefit) A results class for multinomial data fit by l1 regularization
L1MultinomialResultsWrapper(results)
L1NegativeBinomialResults(model, cntfit)
L1NegativeBinomialResultsWrapper(results)
L1PoissonResults(model, cntfit)
L1PoissonResultsWrapper(results)
Logit(endog, exog, **kwargs) Binary choice logit model
LogitResults(model, mlefit[, cov_type, ...]) A results class for Logit Model
MNLogit(endog, exog, **kwargs) Multinomial logit model
MultinomialModel(endog, exog, **kwargs)
MultinomialResults(model, mlefit[, ...]) A results class for multinomial data
MultinomialResultsWrapper(results)
NegativeBinomial(endog, exog[, ...]) Negative Binomial Model for count data
NegativeBinomialResults(model, mlefit[, ...]) A results class for NegativeBinomial 1 and 2
NegativeBinomialResultsWrapper(results)
OLS(endog[, exog, missing, hasconst]) A simple ordinary least squares model.
OrderedModel(endog, exog, **kwargs)
OrderedResults(model, mlefit[, cov_type, ...]) A results class for ordered discrete data.
OrderedResultsWrapper(results)
Poisson(endog, exog[, offset, exposure, missing]) Poisson model for count data
PoissonResults(model, mlefit[, cov_type, ...])
PoissonResultsWrapper(results)
Probit(endog, exog, **kwargs) Binary choice Probit model
ProbitResults(model, mlefit[, cov_type, ...]) A results class for Probit Model
resettable_cache alias of ResettableCache

3.2.2.4. Exceptions

PerfectSeparationError