statsmodels.discrete.discrete_model.MultinomialModel.score MultinomialModel.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.discrete.discrete_model.NegativeBinomial.score NegativeBinomial.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.sandbox.regression.gmm.IVRegressionResults.wresid static IVRegressionResults.wresid()

statsmodels.sandbox.regression.gmm.LinearIVGMM.start_weights LinearIVGMM.start_weights(inv=True)

statsmodels.genmod.families.family.Gamma.predict Gamma.predict(mu) Linear predictors based on given mu values. Parameters: mu : array The mean response variables Returns: lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu.

statsmodels.miscmodels.tmodel.TLinearModel.score_obs TLinearModel.score_obs(params, **kwds) Jacobian/Gradient of log-likelihood evaluated at params for each observation.

Plot Interaction of Categorical Factors Link to Notebook GitHub In this example, we will vizualize the interaction between categorical factors. First, we will create some categorical data are initialized. Then plotted using the interaction_plot function which internally recodes the x-factor categories to ingegers. In [1]: import numpy as np import matplotlib.pyplot as plt import pandas as pd from statsmodels.graphics.factorplots import interaction_plot In [2]: np.random.

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.jac_func NonlinearIVGMM.jac_func(params, weights, args=None, centered=True, epsilon=None) [source]

statsmodels.graphics.regressionplots.plot_fit statsmodels.graphics.regressionplots.plot_fit(results, exog_idx, y_true=None, ax=None, **kwargs) [source] Plot fit against one regressor. This creates one graph with the scatterplot of observed values compared to fitted values. Parameters: results : result instance result instance with resid, model.endog and model.exog as attributes x_var : int or str Name or index of regressor in exog matrix. y_true : array_like (optional) If this is not N

statsmodels.tsa.arima_model.ARMA.fit ARMA.fit(start_params=None, trend='c', method='css-mle', transparams=True, solver='lbfgs', maxiter=50, full_output=1, disp=5, callback=None, **kwargs) [source] Fits ARMA(p,q) model using exact maximum likelihood via Kalman filter. Parameters: start_params : array-like, optional Starting parameters for ARMA(p,q). If None, the default is given by ARMA._fit_start_params. See there for more information. transparams : bool, optional Whehter or not to trans