1.2.15. statsmodels.api.Probit¶
-
class
statsmodels.api.Probit(endog, exog, **kwargs)[source]¶ Binary choice Probit model
Parameters: endog : array-like
1-d endogenous response variable. The dependent variable.
exog : array-like
A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See
statsmodels.tools.add_constant().missing : str
Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none.’
Attributes
endog (array) A reference to the endogenous response variable exog (array) A reference to the exogenous design. -
__init__(endog, exog, **kwargs)¶
-
1.2.15.1. Methods¶
__init__(endog, exog, **kwargs) |
|
cdf(X) |
Probit (Normal) 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) |
Probit model Hessian matrix of the log-likelihood |
information(params) |
Fisher information matrix of model |
initialize() |
Initialize is called by statsmodels.model.LikelihoodModel.__init__ and should contain any preprocessing that needs to be done for a model. |
jac(*args, **kwds) |
jac is deprecated, use score_obs instead! |
loglike(params) |
Log-likelihood of probit model (i.e., the normal distribution). |
loglikeobs(params) |
Log-likelihood of probit model for each observation |
pdf(X) |
Probit (Normal) probability density function |
predict(params[, exog, linear]) |
Predict response variable of a model given exogenous variables. |
score(params) |
Probit model score (gradient) vector |
score_obs(params) |
Probit model Jacobian for each observation |
1.2.15.2. Attributes¶
endog_names |
|
exog_names |