statsmodels.miscmodels.count.PoissonZiGMLE.expandparams PoissonZiGMLE.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 gene

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.genmod.cov_struct.Independence.covariance_matrix Independence.covariance_matrix(expval, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : The covariance o

Input-Output iolib statsmodels offers some functions for input and output. These include a reader for STATA files, a class for generating tables for printing in several formats and two helper functions for pickling. Users can also leverage the powerful input/output functions provided by pandas.io. Among other things, pandas (a statsmodels dependency) allows reading and writing to Excel, CSV, and HDF5 (PyTables). Examples SimpleTable: Basic example Module Reference foreign.StataReader(f

statsmodels.miscmodels.count.PoissonZiGMLE.nloglikeobs PoissonZiGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.robust.robust_linear_model.RLMResults.fittedvalues static RLMResults.fittedvalues() [source]

statsmodels.duration.hazard_regression.PHReg.from_formula classmethod PHReg.from_formula(formula, data, status=None, entry=None, strata=None, offset=None, subset=None, ties='breslow', missing='drop', *args, **kwargs) [source] Create a proportional hazards regression model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. status : array-like The censoring status values;

statsmodels.sandbox.distributions.transformed.TransfTwo_gen.logcdf TransfTwo_gen.logcdf(x, *args, **kwds) Log of the cumulative distribution 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: logcdf : array

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.forecast DynamicVAR.forecast(steps=1) [source] Produce dynamic forecast Parameters: steps : Returns: forecasts : pandas.DataFrame

statsmodels.genmod.families.links.probit class statsmodels.genmod.families.links.probit(dbn=) [source] The probit (standard normal CDF) transform Notes g(p) = scipy.stats.norm.ppf(p) probit is an alias of CDFLink. Methods deriv(p) Derivative of CDF link deriv2(p) Second derivative of the link function g??(p) inverse(z) The inverse of the CDF link inverse_deriv(z) Derivative of the inverse of the CDF transformation link function