statsmodels.sandbox.regression.gmm.IVGMM.score IVGMM.score(params, weights, epsilon=None, centered=True)

statsmodels.genmod.generalized_estimating_equations.GEEResults.split_resid static GEEResults.split_resid() Returns the residuals, the endogeneous data minus the fitted values from the model. The residuals are returned as a list of arrays containing the residuals for each cluster.

statsmodels.nonparametric.kde.KDEUnivariate.sf static KDEUnivariate.sf() [source] Returns the survival function evaluated at the support. Notes Will not work if fit has not been called.

Generalized Linear Models Generalized linear models currently supports estimation using the one-parameter exponential families See Module Reference for commands and arguments. Examples # Load modules and data import statsmodels.api as sm data = sm.datasets.scotland.load() data.exog = sm.add_constant(data.exog) # Instantiate a gamma family model with the default link function. gamma_model = sm.GLM(data.endog, data.exog, family=sm.families.Gamma()) gamma_results = gamma_model.fit() Detailed ex

statsmodels.tsa.arima_model.ARMA.hessian ARMA.hessian(params) [source] Compute the Hessian at params, Notes This is a numerical approximation.

statsmodels.sandbox.sysreg.SUR class statsmodels.sandbox.sysreg.SUR(sys, sigma=None, dfk=None) [source] Seemingly Unrelated Regression Parameters: sys : list [endog1, exog1, endog2, exog2,...] It will be of length 2 x M, where M is the number of equations endog = exog. sigma : array-like M x M array where sigma[i,j] is the covariance between equation i and j dfk : None, ?dfk1?, or ?dfk2? Default is None. Correction for the degrees of freedom should be specified for small samples. See t

statsmodels.discrete.discrete_model.MultinomialResults.t_test MultinomialResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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 :

statsmodels.sandbox.distributions.extras.ACSkewT_gen.nnlf ACSkewT_gen.nnlf(theta, x) Return negative loglikelihood function Notes This is -sum(log pdf(x, theta), axis=0) where theta are the parameters (including loc and scale).

statsmodels.regression.linear_model.OLSResults.t_test OLSResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. 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 arra

statsmodels.sandbox.stats.runs.mcnemar statsmodels.sandbox.stats.runs.mcnemar(x, y=None, exact=True, correction=True) [source] McNemar test Parameters: x, y : array_like two paired data samples. If y is None, then x can be a 2 by 2 contingency table. x and y can have more than one dimension, then the results are calculated under the assumption that axis zero contains the observation for the samples. exact : bool If exact is true, then the binomial distribution will be used. If exact is f