statsmodels.discrete.discrete_model.Probit.fit Probit.fit(start_params=None, method='newton', maxiter=35, full_output=1, disp=1, callback=None, **kwargs) [source] Fit the model using maximum likelihood. The rest of the docstring is from statsmodels.base.model.LikelihoodModel.fit Fit method for likelihood based models Parameters: start_params : array-like, optional Initial guess of the solution for the loglikelihood maximization. The default is an array of zeros. method : str, optional Th

statsmodels.discrete.discrete_model.Probit.fit_regularized Probit.fit_regularized(start_params=None, method='l1', maxiter='defined_by_method', full_output=1, disp=1, callback=None, alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03, **kwargs) Fit the model using a regularized maximum likelihood. The regularization method AND the solver used is determined by the argument method. Parameters: start_params : array-like, optional Initial guess of the solution for t

statsmodels.discrete.discrete_model.Probit.cov_params_func_l1 Probit.cov_params_func_l1(likelihood_model, xopt, retvals) Computes cov_params on a reduced parameter space corresponding to the nonzero parameters resulting from the l1 regularized fit. Returns a full cov_params matrix, with entries corresponding to zero?d values set to np.nan.

statsmodels.genmod.families.links.probit.deriv2 probit.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.genmod.families.links.probit.deriv probit.deriv(p) Derivative of CDF link Parameters: p : array-like mean parameters Returns: g?(p) : array The derivative of CDF transform at p Notes g?(p) = 1./ dbn.pdf(dbn.ppf(p))

Prediction (out of sample) Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm Artificial data In [2]: nsample = 50 sig = 0.25 x1 = np.linspace(0, 20, nsample) X = np.column_stack((x1, np.sin(x1), (x1-5)**2)) X = sm.add_constant(X) beta = [5., 0.5, 0.5, -0.02] y_true = np.dot(X, beta) y = y_true + sig * np.random.normal(size=nsample) Estimation In [3]: olsmod = sm.OLS(y, X) olsres = olsmo

statsmodels.genmod.families.links.Power.inverse_deriv Power.inverse_deriv(z) [source] Derivative of the inverse of the power transform Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the power transform : function :

statsmodels.discrete.discrete_model.Probit.cdf Probit.cdf(X) [source] Probit (Normal) cumulative distribution function Parameters: X : array-like The linear predictor of the model (XB). Returns: cdf : ndarray The cdf evaluated at X. Notes This function is just an alias for scipy.stats.norm.cdf

statsmodels.genmod.families.links.Power.inverse Power.inverse(z) [source] Inverse of the power transform link function Parameters: `z` : array-like Value of the transformed mean parameters at p Returns: `p` : array Mean parameters Notes g^(-1)(z`) = z`**(1/`power)

statsmodels.genmod.families.links.Power.deriv Power.deriv(p) [source] Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)