Multinomial logit model
Parameters: | endog : array-like
exog : array-like
missing : str
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Notes
See developer notes for further information on MNLogit internals.
Attributes
endog | array | A reference to the endogenous response variable |
exog | array | A reference to the exogenous design. |
J | float | The number of choices for the endogenous variable. Note that this is zero-indexed. |
K | float | The actual number of parameters for the exogenous design. Includes the constant if the design has one. |
names | dict | A dictionary mapping the column number in wendog to the variables in endog. |
wendog | array | An n x j array where j is the number of unique categories in endog. Each column of j is a dummy variable indicating the category of each observation. See names for a dictionary mapping each column to its category. |
Methods
cdf(X) | Multinomial logit cumulative distribution function. |
cov_params_func_l1(likelihood_model, xopt, ...) | Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. |
fit([start_params, method, maxiter, ...]) | Fit the model using maximum likelihood. |
fit_regularized([start_params, method, ...]) | Fit the model using a regularized maximum likelihood. |
from_formula(formula, data[, subset]) | Create a Model from a formula and dataframe. |
hessian(params) | Multinomial logit Hessian matrix of the log-likelihood |
information(params) | Fisher information matrix of model |
initialize() | Preprocesses the data for MNLogit. |
jac(params) | Jacobian matrix for multinomial logit model log-likelihood |
loglike(params) | Log-likelihood of the multinomial logit model. |
loglikeobs(params) | Log-likelihood of the multinomial logit model for each observation. |
pdf(eXB) | NotImplemented |
predict(params[, exog, linear]) | Predict response variable of a model given exogenous variables. |
score(params) | Score matrix for multinomial logit model log-likelihood |
Attributes
endog_names | |
exog_names |