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.
- 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) |
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BinaryResults(model, mlefit[, cov_type, ...]) |
A results class for binary data |
BinaryResultsWrapper(results) |
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CountModel(endog, exog[, offset, exposure, ...]) |
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CountResults(model, mlefit[, cov_type, ...]) |
A results class for count data |
CountResultsWrapper(results) |
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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) |
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L1CountResults(model, cntfit) |
A results class for count data fit by l1 regularization |
L1CountResultsWrapper(results) |
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L1MultinomialResults(model, mlefit) |
A results class for multinomial data fit by l1 regularization |
L1MultinomialResultsWrapper(results) |
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L1NegativeBinomialResults(model, cntfit) |
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L1NegativeBinomialResultsWrapper(results) |
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L1PoissonResults(model, cntfit) |
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L1PoissonResultsWrapper(results) |
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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) |
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MultinomialResults(model, mlefit[, ...]) |
A results class for multinomial data |
MultinomialResultsWrapper(results) |
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NegativeBinomial(endog, exog[, ...]) |
Negative Binomial Model for count data |
NegativeBinomialResults(model, mlefit[, ...]) |
A results class for NegativeBinomial 1 and 2 |
NegativeBinomialResultsWrapper(results) |
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OLS(endog[, exog, missing, hasconst]) |
A simple ordinary least squares model. |
OrderedModel(endog, exog, **kwargs) |
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OrderedResults(model, mlefit[, cov_type, ...]) |
A results class for ordered discrete data. |
OrderedResultsWrapper(results) |
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Poisson(endog, exog[, offset, exposure, missing]) |
Poisson model for count data |
PoissonResults(model, mlefit[, cov_type, ...]) |
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PoissonResultsWrapper(results) |
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Probit(endog, exog, **kwargs) |
Binary choice Probit model |
ProbitResults(model, mlefit[, cov_type, ...]) |
A results class for Probit Model |
resettable_cache |
alias of ResettableCache |