statsmodels.sandbox.distributions.transformed.ExpTransf_gen.std ExpTransf_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: std : float standard deviation of the distribution

statsmodels.sandbox.regression.gmm.GMMResults.jval static GMMResults.jval() [source]

statsmodels.discrete.discrete_model.LogitResults.wald_test LogitResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tu

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

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

statsmodels.sandbox.regression.gmm.GMMResults.pvalues static GMMResults.pvalues()

statsmodels.discrete.discrete_model.Poisson.from_formula classmethod Poisson.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args :

statsmodels.tsa.vector_ar.var_model.VARResults.plot_acorr VARResults.plot_acorr(nlags=10, linewidth=8) Plot theoretical autocorrelation function