statsmodels.discrete.discrete_model.CountResults class statsmodels.discrete.discrete_model.CountResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for count data Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criterion.

statsmodels.regression.linear_model.RegressionResults.mse_model static RegressionResults.mse_model() [source]

ANOVA Analysis of Variance models Examples In [1]: import statsmodels.api as sm In [2]: from statsmodels.formula.api import ols In [3]: moore = sm.datasets.get_rdataset("Moore", "car", ...: cache=True) # load data ...: In [4]: data = moore.data In [5]: data = data.rename(columns={"partner.status" : ...: "partner_status"}) # make name pythonic ...: In [6]: moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_sta

statsmodels.genmod.generalized_estimating_equations.GEEResults.tvalues static GEEResults.tvalues() Return the t-statistic for a given parameter estimate.

statsmodels.duration.hazard_regression.PHReg.efron_loglike PHReg.efron_loglike(params) [source] Returns the value of the log partial likelihood function evaluated at params, using the Efron method to handle tied times.

statsmodels.discrete.discrete_model.ProbitResults class statsmodels.discrete.discrete_model.ProbitResults(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None) [source] A results class for Probit Model Parameters: model : A DiscreteModel instance params : array-like The parameters of a fitted model. hessian : array-like The hessian of the fitted model. scale : float A scale parameter for the covariance matrix. Returns: *Attributes* : aic : float Akaike information criteri

statsmodels.sandbox.regression.gmm.IVGMM.fititer IVGMM.fititer(start, maxiter=2, start_invweights=None, weights_method='cov', wargs=(), optim_method='bfgs', optim_args=None) iterative estimation with updating of optimal weighting matrix stopping criteria are maxiter or change in parameter estimate less than self.epsilon_iter, with default 1e-6. Parameters: start : array starting value for parameters maxiter : int maximum number of iterations start_weights : array (nmoms, nmoms) initial

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.regression.quantile_regression.QuantReg.whiten QuantReg.whiten(data) [source] QuantReg model whitener does nothing: returns data.

statsmodels.discrete.discrete_model.BinaryResults.summary2 BinaryResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental function to summarize regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title a