statsmodels.discrete.discrete_model.BinaryModel.cov_params_func_l1 BinaryModel.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.discrete.discrete_model.BinaryModel.cdf BinaryModel.cdf(X) The cumulative distribution function of the model.

statsmodels.genmod.cov_struct.Autoregressive.update Autoregressive.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.genmod.cov_struct.Autoregressive.summary Autoregressive.summary() [source]

statsmodels.genmod.cov_struct.Autoregressive.initialize Autoregressive.initialize(model) Called by GEE, used by implementations that need additional setup prior to running fit. Parameters: model : GEE class A reference to the parent GEE class instance.

statsmodels.genmod.cov_struct.Autoregressive.covariance_matrix_solve Autoregressive.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each

statsmodels.genmod.cov_struct.Autoregressive.covariance_matrix Autoregressive.covariance_matrix(endog_expval, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : The co

Autoregressive Moving Average (ARMA): Sunspots data Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np from scipy import stats import pandas as pd import matplotlib.pyplot as plt import statsmodels.api as sm In [2]: from statsmodels.graphics.api import qqplot Sunpots Data In [3]: print(sm.datasets.sunspots.NOTE) :: Number of Observations - 309 (Annual 1700 - 2008) Number of Variables - 1 Variable na

Autoregressive Moving Average (ARMA): Artificial data Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm import pandas as pd from statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) Generate some data from an ARMA process: In [2]: arparams = np.array([.75, -.25]) maparams = np.array([.65, .35]) The conventions of the arma_generate function require that we specify a 1 f

statsmodels.tsa.ar_model.ARResults.wald_test ARResults.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 tuple of arrays in