statsmodels.tsa.vector_ar.var_model.VARResults.plot VARResults.plot() [source] Plot input time series

statsmodels.stats.power.FTestAnovaPower.power FTestAnovaPower.power(effect_size, nobs, alpha, k_groups=2) [source] Calculate the power of a F-test for one factor ANOVA. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the N

statsmodels.stats.proportion.proportions_chisquare statsmodels.stats.proportion.proportions_chisquare(count, nobs, value=None) [source] test for proportions based on chisquare test Parameters: count : integer or array_like the number of successes in nobs trials. If this is array_like, then the assumption is that this represents the number of successes for each independent sample nobs : integer the number of trials or observations, with the same length as count. value : None or float or

statsmodels.genmod.families.family.NegativeBinomial.loglike NegativeBinomial.loglike(endog, lin_pred=None) [source] The loglikelihood function for the negative binomial family. Parameters: endog : array-like Endogenous response variable lin_pred : array-like The linear predictor of the model. This is dot(exog,params), plus the offset if present. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes sum(endog*log(alpha*exp

statsmodels.genmod.families.family.Gamma class statsmodels.genmod.families.family.Gamma(link=) [source] Gamma exponential family distribution. Parameters: link : a link instance, optional The default link for the Gamma family is the inverse link. Available links are log, identity, and inverse. See statsmodels.family.links for more information. See also statsmodels.genmod.families.family.Family, Link Functions Attributes Gamma.link a link instance The link function of the Gamma instance

statsmodels.genmod.generalized_linear_model.GLMResults.cov_params GLMResults.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-li

statsmodels.sandbox.distributions.transformed.Transf_gen.freeze Transf_gen.freeze(*args, **kwds) Freeze the distribution for the given arguments. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution. Should include all the non-optional arguments, may include loc and scale. Returns: rv_frozen : rv_frozen instance The frozen distribution.

statsmodels.discrete.discrete_model.MultinomialResults.prsquared static MultinomialResults.prsquared()

statsmodels.discrete.discrete_model.BinaryResults.get_margeff BinaryResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regressor. ?al

statsmodels.robust.norms.estimate_location statsmodels.robust.norms.estimate_location(a, scale, norm=None, axis=0, initial=None, maxiter=30, tol=1e-06) [source] M-estimator of location using self.norm and a current estimator of scale. This iteratively finds a solution to norm.psi((a-mu)/scale).sum() == 0 Parameters: a : array Array over which the location parameter is to be estimated scale : array Scale parameter to be used in M-estimator norm : RobustNorm, optional Robust norm used in