statsmodels.sandbox.distributions.transformed.LogTransf_gen.pdf LogTransf_gen.pdf(x, *args, **kwds) Probability density function at x of the given RV. Parameters: x : array_like quantiles 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: pdf : ndarray Probability density

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

statsmodels.regression.quantile_regression.QuantRegResults.remove_data QuantRegResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time

statsmodels.sandbox.regression.gmm.IVGMMResults.resid static IVGMMResults.resid() [source]

statsmodels.sandbox.regression.gmm.IVRegressionResults.load classmethod IVRegressionResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.sandbox.regression.gmm.IVGMMResults.summary IVGMMResults.summary(yname=None, xname=None, title=None, alpha=0.05) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns: smry : Summary in

statsmodels.regression.quantile_regression.QuantRegResults.f_test QuantRegResults.f_test(r_matrix, cov_p=None, scale=1.0, invcov=None) Compute the F-test for a joint linear hypothesis. This is a special case of wald_test that always uses the F distribution. 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 hypothes

statsmodels.tsa.ar_model.ARResults.cov_params ARResults.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-like, optional Must be

statsmodels.sandbox.distributions.extras.SkewNorm_gen.stats SkewNorm_gen.stats(*args, **kwds) Some statistics of the given RV Parameters: 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 (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which moments t

statsmodels.discrete.discrete_model.MultinomialResults.resid_misclassified static MultinomialResults.resid_misclassified() [source] Residuals indicating which observations are misclassified. Notes The residuals for the multinomial model are defined as where is the index of the category for the endogenous variable and is the index of the predicted probabilities for each category. That is, the residual is a binary indicator that is 0 if the category with the highest predicted probability