statsmodels.discrete.discrete_model.MNLogit.initialize MNLogit.initialize() Preprocesses the data for MNLogit.

statsmodels.iolib.table.SimpleTable.extend SimpleTable.extend() L.extend(iterable) ? extend list by appending elements from the iterable

statsmodels.genmod.generalized_linear_model.GLM.fit_constrained GLM.fit_constrained(constraints, start_params=None, **fit_kwds) [source] fit the model subject to linear equality constraints The constraints are of the form R params = q where R is the constraint_matrix and q is the vector of constraint_values. The estimation creates a new model with transformed design matrix, exog, and converts the results back to the original parameterization. Parameters: constraints : formula expression or

statsmodels.iolib.summary.Summary.as_csv Summary.as_csv() [source] return tables as string Returns: csv : string concatenated summary tables in comma delimited format

statsmodels.genmod.cov_struct.GlobalOddsRatio.update GlobalOddsRatio.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.sandbox.distributions.extras.skewnorm2 statsmodels.sandbox.distributions.extras.skewnorm2 = univariate Skew-Normal distribution of Azzalini class follows scipy.stats.distributions pattern

statsmodels.stats.power.FTestAnovaPower class statsmodels.stats.power.FTestAnovaPower(**kwds) [source] Statistical Power calculations F-test for one factor balanced ANOVA Methods plot_power([dep_var, nobs, effect_size, ...]) plot power with number of observations or effect size on x-axis power(effect_size, nobs, alpha[, k_groups]) Calculate the power of a F-test for one factor ANOVA. solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a F-test

statsmodels.regression.quantile_regression.QuantRegResults.summary QuantRegResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Retu

statsmodels.graphics.boxplots.violinplot statsmodels.graphics.boxplots.violinplot(data, ax=None, labels=None, positions=None, side='both', show_boxplot=True, plot_opts={}) [source] Make a violin plot of each dataset in the data sequence. A violin plot is a boxplot combined with a kernel density estimate of the probability density function per point. Parameters: data : sequence of ndarrays Data arrays, one array per value in positions. ax : Matplotlib AxesSubplot instance, optional If giv

statsmodels.regression.linear_model.GLSAR.fit GLSAR.fit(method='pinv', cov_type='nonrobust', cov_kwds=None, use_t=None, **kwargs) Full fit of the model. The results include an estimate of covariance matrix, (whitened) residuals and an estimate of scale. Parameters: method : str Can be ?pinv?, ?qr?. ?pinv? uses the Moore-Penrose pseudoinverse to solve the least squares problem. ?qr? uses the QR factorization. Returns: A RegressionResults class instance. : See also regression.Regression