statsmodels.sandbox.distributions.extras.SkewNorm2_gen.ppf SkewNorm2_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability 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: x : array_like

statsmodels.sandbox.regression.gmm.IVRegressionResults.normalized_cov_params IVRegressionResults.normalized_cov_params()

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.fit ExpTransf_gen.fit(data, *args, **kwds) Return MLEs for shape, location, and scale parameters from data. MLE stands for Maximum Likelihood Estimate. Starting estimates for the fit are given by input arguments; for any arguments not provided with starting estimates, self._fitstart(data) is called to generate such. One can hold some parameters fixed to specific values by passing in keyword arguments f0, f1, ..., fn (for shape param

statsmodels.sandbox.tsa.fftarma.ArmaFft.impulse_response ArmaFft.impulse_response(nobs=None) get the impulse response function (MA representation) for ARMA process Parameters: ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns: ir : array, 1d impulse response function with nobs elements Notes This is the same as finding the MA representation of an ARMA(p,q). By reversing the r

statsmodels.discrete.discrete_model.LogitResults.initialize LogitResults.initialize(model, params, **kwd)

statsmodels.regression.linear_model.WLS.loglike WLS.loglike(params) [source] Returns the value of the gaussian log-likelihood function at params. Given the whitened design matrix, the log-likelihood is evaluated at the parameter vector params for the dependent variable Y. Parameters: params : array-like The parameter estimates. Returns: llf : float The value of the log-likelihood function for a WLS Model. Notes where is a diagonal matrix

statsmodels.regression.linear_model.WLS.initialize WLS.initialize()

statsmodels.stats.weightstats.CompareMeans class statsmodels.stats.weightstats.CompareMeans(d1, d2) [source] class for two sample comparison The tests and the confidence interval work for multi-endpoint comparison: If d1 and d2 have the same number of rows, then each column of the data in d1 is compared with the corresponding column in d2. Parameters: d1, d2 : instances of DescrStatsW Notes The result for the statistical tests and the confidence interval are independent of the user specifie

statsmodels.duration.hazard_regression.PHRegResults.cov_params PHRegResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-lik

statsmodels.genmod.families.links.Power.inverse_deriv Power.inverse_deriv(z) [source] Derivative of the inverse of the power transform Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the power transform : function :