statsmodels.iolib.summary2.Summary.add_dict Summary.add_dict(d, ncols=2, align='l', float_format='%.4f') [source] Add the contents of a Dict to summary table Parameters: d : dict Keys and values are automatically coerced to strings with str(). Users are encouraged to format them before using add_dict. ncols: int : Number of columns of the output table align : string Data alignment (l/c/r)

statsmodels.tsa.vector_ar.dynamic.DynamicVAR class statsmodels.tsa.vector_ar.dynamic.DynamicVAR(data, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None) [source] Estimates time-varying vector autoregression (VAR(p)) using equation-by-equation least squares Parameters: data : pandas.DataFrame lag_order : int, default 1 window : int window_type : {?expanding?, ?rolling?} min_periods : int or None Minimum number of observations to require in window, defaults to wi

statsmodels.sandbox.distributions.transformed.LogTransf_gen.moment LogTransf_gen.moment(n, *args, **kwds) n?th order non-central moment of distribution. Parameters: n : int, n>=1 Order of moment. arg1, arg2, arg3,... : float The shape parameter(s) for the distribution (see docstring of the instance object for more information). kwds : keyword arguments, optional These can include ?loc? and ?scale?, as well as other keyword arguments relevant for a given distribution.

statsmodels.genmod.cov_struct.Autoregressive.covariance_matrix_solve Autoregressive.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each

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.regression.mixed_linear_model.MixedLM.hessian_sqrt MixedLM.hessian_sqrt(params) [source] Returns the Hessian matrix of the log-likelihood evaluated at a given point, calculated with respect to the parameterization in which the random effects covariance matrix is represented through its Cholesky square root. Parameters: params : MixedLMParams or array-like The model parameters. If array-like, must contain packed parameters that are compatible with this model. Returns: The Hes

statsmodels.tsa.arima_process.ArmaProcess.invertroots ArmaProcess.invertroots(retnew=False) [source] make MA polynomial invertible by inverting roots inside unit circle Parameters: retnew : boolean If False (default), then return the lag-polynomial as array. If True, then return a new instance with invertible MA-polynomial Returns: manew : array new invertible MA lag-polynomial, returned if retnew is false. wasinvertible : boolean True if the MA lag-polynomial was already invertible,

statsmodels.regression.quantile_regression.QuantRegResults.rsquared_adj static QuantRegResults.rsquared_adj() [source]

statsmodels.tsa.ar_model.AR.score AR.score(params) [source] Return the gradient of the loglikelihood at params. Parameters: params : array-like The parameter values at which to evaluate the score function. Notes Returns numerical gradient.

statsmodels.tools.numdiff.approx_hess_cs statsmodels.tools.numdiff.approx_hess_cs(x, f, epsilon=None, args=(), kwargs={}) [source] Calculate Hessian with complex-step derivative approximation Calculate Hessian with finite difference derivative approximation Parameters: x : array_like value at which function derivative is evaluated f : function function of one array f(x, *args, **kwargs) epsilon : float or array-like, optional Stepsize used, if None, then stepsize is automatically chose