statsmodels.discrete.discrete_model.LogitResults.cov_params LogitResults.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-like,

statsmodels.regression.quantile_regression.QuantRegResults.condition_number static QuantRegResults.condition_number() Return condition number of exogenous matrix. Calculated as ratio of largest to smallest eigenvalue.

statsmodels.discrete.discrete_model.MultinomialModel.loglike MultinomialModel.loglike(params) Log-likelihood of model.

statsmodels.regression.linear_model.OLSResults.summary2 OLSResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental summary function to summarize the 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 ti

statsmodels.graphics.gofplots.ProbPlot.ppplot ProbPlot.ppplot(xlabel=None, ylabel=None, line=None, other=None, ax=None, **plotkwargs) [source] P-P plot of the percentiles (probabilities) of x versus the probabilities (percetiles) of a distribution. Parameters: xlabel, ylabel : str or None, optional User-provided lables for the x-axis and y-axis. If None (default), other values are used depending on the status of the kwarg other. line : str {?45?, ?s?, ?r?, q?} or None, optional Options f

statsmodels.graphics.tsaplots.month_plot statsmodels.graphics.tsaplots.month_plot(x, dates=None, ylabel=None, ax=None) [source] Seasonal plot of monthly data Parameters: x : array-like Seasonal data to plot. If dates is None, x must be a pandas object with a PeriodIndex or DatetimeIndex with a monthly frequency. dates : array-like, optional If x is not a pandas object, then dates must be supplied. ylabel : str, optional The label for the y-axis. Will attempt to use the name attribute o

statsmodels.miscmodels.tmodel.TLinearModel.nloglikeobs TLinearModel.nloglikeobs(params) [source] Loglikelihood of linear model with t distributed errors. Parameters: params : array The parameters of the model. The last 2 parameters are degrees of freedom and scale. Returns: loglike : array, (nobs,) The log likelihood of the model evaluated at params for each observation defined by self.endog and self.exog. Notes The t distribution is the standard t distribution and not a standardi

statsmodels.tools.eval_measures.aicc statsmodels.tools.eval_measures.aicc(llf, nobs, df_modelwc) [source] Akaike information criterion (AIC) with small sample correction Parameters: llf : float value of the loglikelihood nobs : int number of observations df_modelwc : int number of parameters including constant Returns: aicc : float information criterion References http://en.wikipedia.org/wiki/Akaike_information_criterion#AICc

statsmodels.stats.outliers_influence.OLSInfluence.ess_press static OLSInfluence.ess_press() [source] (cached attribute) error sum of squares of PRESS residuals

statsmodels.genmod.families.family.Family.resid_anscombe Family.resid_anscombe(endog, mu) [source] The Anscome residuals. See also statsmodels.families.family.Family, for