statsmodels.stats.correlation_tools.corr_nearest statsmodels.stats.correlation_tools.corr_nearest(corr, threshold=1e-15, n_fact=100) [source] Find the nearest correlation matrix that is positive semi-definite. The function iteratively adjust the correlation matrix by clipping the eigenvalues of a difference matrix. The diagonal elements are set to one. Parameters: corr : ndarray, (k, k) initial correlation matrix threshold : float clipping threshold for smallest eigenvalue, see Notes n_

statsmodels.tools.numdiff.approx_hess3 statsmodels.tools.numdiff.approx_hess3(x, f, epsilon=None, args=(), kwargs={}) [source] 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 chosen according to EPS**(1/4)*x. args : tuple Arguments for functio

statsmodels.sandbox.regression.try_catdata.groupstatsbin statsmodels.sandbox.regression.try_catdata.groupstatsbin(factors, values) [source] uses np.bincount, assumes factors/labels are integers

statsmodels.stats.power.tt_ind_solve_power statsmodels.stats.power.tt_ind_solve_power = > solve for any one parameter of the power of a two sample t-test for t-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be None, all others need numeric values Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviation. effect_size has to be positive. nobs1 : int or float number of observations of

statsmodels.genmod.families.family.InverseGaussian class statsmodels.genmod.families.family.InverseGaussian(link=) [source] InverseGaussian exponential family. Parameters: link : a link instance, optional The default link for the inverse Gaussian family is the inverse squared link. Available links are inverse_squared, inverse, log, and identity. See statsmodels.family.links for more information. See also statsmodels.genmod.families.family.Family, Link Functions Notes The inverse Guassi

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

statsmodels.sandbox.distributions.extras.SkewNorm_gen.pdf SkewNorm_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 functio

statsmodels.iolib.summary2.Summary.add_array Summary.add_array(array, align='r', float_format='%.4f') [source] Add the contents of a Numpy array to summary table Parameters: array : numpy array (2D) float_format: string : Formatting to array if type is float align : string Data alignment (l/c/r)

statsmodels.tsa.vector_ar.var_model.VARResults.resid_acov VARResults.resid_acov(nlags=1) [source] Compute centered sample autocovariance (including lag 0) Parameters: nlags : int

statsmodels.discrete.discrete_model.MNLogit.predict MNLogit.predict(params, exog=None, linear=False) Predict response variable of a model given exogenous variables. Parameters: params : array-like 2d array of fitted parameters of the model. Should be in the order returned from the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous variables. If you only hav