statsmodels.regression.linear_model.OLS.initialize OLS.initialize()

statsmodels.genmod.families.links.Link class statsmodels.genmod.families.links.Link [source] A generic link function for one-parameter exponential family. Link does nothing, but lays out the methods expected of any subclass. Methods deriv(p) Derivative of the link function g?(p). deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of the link function. inverse_deriv(z) Derivative of the inverse link function g^(-1)(z).

statsmodels.sandbox.regression.anova_nistcertified.anova_oneway statsmodels.sandbox.regression.anova_nistcertified.anova_oneway(y, x, seq=0) [source]

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.stats.outliers_influence.OLSInfluence.hat_matrix_diag static OLSInfluence.hat_matrix_diag() [source] (cached attribute) diagonal of the hat_matrix for OLS Notes temporarily calculated here, this should go to model class

statsmodels.regression.linear_model.OLSResults.mse_model static OLSResults.mse_model()

statsmodels.tools.eval_measures.aicc_sigma statsmodels.tools.eval_measures.aicc_sigma(sigma2, nobs, df_modelwc, islog=False) [source] Akaike information criterion (AIC) with small sample correction Parameters: sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including con

statsmodels.regression.mixed_linear_model.MixedLM.score MixedLM.score(params) [source] Returns the score vector of the profile log-likelihood. Notes The score vector that is returned is computed with respect to the parameterization defined by this model instance?s use_sqrt attribute. The input value params can be with respect to any parameterization.

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.median ExpTransf_gen.median(*args, **kwds) Median of the distribution. 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 is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distribut

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.logpdf ExpTransf_gen.logpdf(x, *args, **kwds) Log of the probability density function at x of the given RV. This uses a more numerically accurate calculation if available. 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, opti