statsmodels.regression.quantile_regression.QuantRegResults.pvalues static QuantRegResults.pvalues()

statsmodels.graphics.gofplots.ProbPlot class statsmodels.graphics.gofplots.ProbPlot(data, dist=, fit=False, distargs=(), a=0, loc=0, scale=1) [source] Class for convenient construction of Q-Q, P-P, and probability plots. Can take arguments specifying the parameters for dist or fit them automatically. (See fit under kwargs.) Parameters: data : array-like 1d data array dist : A scipy.stats or statsmodels distribution Compare x against dist. The default is scipy.stats.distributions.norm (a

statsmodels.graphics.tsaplots.plot_pacf statsmodels.graphics.tsaplots.plot_pacf(x, ax=None, lags=None, alpha=0.05, method='ywm', use_vlines=True, **kwargs) [source] Plot the partial autocorrelation function Plots lags on the horizontal and the correlations on vertical axis. Parameters: x : array_like Array of time-series values ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. lags : array_like, optional Arra

statsmodels.discrete.discrete_model.Poisson.cov_params_func_l1 Poisson.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.genmod.cov_struct.Exchangeable.update Exchangeable.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.families.links.Link.deriv2 Link.deriv2(p) [source] Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.tsa.arima_model.ARMAResults.fittedvalues static ARMAResults.fittedvalues() [source]

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.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.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 :