statsmodels.discrete.discrete_model.DiscreteResults.bse static DiscreteResults.bse()

statsmodels.tsa.vector_ar.var_model.VARResults.detomega static VARResults.detomega() [source] Return determinant of white noise covariance with degrees of freedom correction:

statsmodels.stats.diagnostic.het_arch statsmodels.stats.diagnostic.het_arch(resid, maxlag=None, autolag=None, store=False, regresults=False, ddof=0) Engle?s Test for Autoregressive Conditional Heteroscedasticity (ARCH) Parameters: resid : ndarray, (nobs,) residuals from an estimation, or time series maxlag : int highest lag to use autolag : None or string If None, then a fixed number of lags given by maxlag is used. store : bool If true then the intermediate results are also returned

statsmodels.regression.quantile_regression.QuantRegResults.bic static QuantRegResults.bic() [source]

statsmodels.stats.sandwich_covariance.se_cov statsmodels.stats.sandwich_covariance.se_cov(cov) get standard deviation from covariance matrix just a shorthand function np.sqrt(np.diag(cov)) Parameters: cov : array_like, square covariance matrix Returns: std : ndarray standard deviation from diagonal of cov

statsmodels.iolib.foreign.StataReader.variables StataReader.variables() [source] Returns a list of the dataset?s StataVariables objects.

statsmodels.miscmodels.tmodel.TLinearModel.expandparams TLinearModel.expandparams(params) expand to full parameter array when some parameters are fixed Parameters: params : array reduced parameter array Returns: paramsfull : array expanded parameter array where fixed parameters are included Notes Calling this requires that self.fixed_params and self.fixed_paramsmask are defined. developer notes: This can be used in the log-likelihood to ... this could also be replaced by a more gener

statsmodels.discrete.discrete_model.MultinomialModel.predict MultinomialModel.predict(params, exog=None, linear=False) [source] Predict response variable of a model given exogenous variables. Parameters: params : array-like 2d array of fitted parameters of the model. Should be in the order returned from the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous

Ordinary Least Squares Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm import matplotlib.pyplot as plt from statsmodels.sandbox.regression.predstd import wls_prediction_std np.random.seed(9876789) OLS estimation Artificial data: In [2]: nsample = 100 x = np.linspace(0, 10, 100) X = np.column_stack((x, x**2)) beta = np.array([1, 0.1, 10]) e = np.random.normal(size=nsample) Our model needs an i

statsmodels.discrete.discrete_model.Poisson.score_obs Poisson.score_obs(params) [source] Poisson model Jacobian of the log-likelihood for each observation Parameters: params : array-like The parameters of the model Returns: score : ndarray (nobs, k_vars) The score vector of the model evaluated at params Notes for observations where the loglinear model is assumed