statsmodels.genmod.cov_struct.Exchangeable.covariance_matrix Exchangeable.covariance_matrix(expval, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : The covariance o

endog, exog, what?s that? Statsmodels is using endog and exog as names for the data, the observed variables that are used in an estimation problem. Other names that are often used in different statistical packages or text books are, for example, endog exog y x y variable x variable left hand side (LHS) right hand side (RHS) dependent variable independent variable regressand regressors outcome design response variable explanatory variable The usage is quite often domain and model specific; how

statsmodels.genmod.cov_struct.Exchangeable.covariance_matrix_solve Exchangeable.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each obse

statsmodels.emplike.descriptive.DescStatMV class statsmodels.emplike.descriptive.DescStatMV(endog) [source] A class for conducting inference on multivariate means and correlation. Parameters: endog : ndarray Data to be analyzed Attributes endog ndarray Data to be analyzed nobs float Number of observations Methods ci_corr([sig, upper_bound, lower_bound]) Returns the confidence intervals for the correlation coefficient mv_mean_contour(mu1_low, mu1_upp, mu2_low, ...) Creates a confidence

statsmodels.emplike.descriptive.DescStatUV class statsmodels.emplike.descriptive.DescStatUV(endog) [source] A class to compute confidence intervals and hypothesis tests involving mean, variance, kurtosis and skewness of a univariate random variable. Parameters: endog : 1darray Data to be analyzed Attributes endog 1darray Data to be analyzed nobs float Number of observations Methods ci_kurt([sig, upper_bound, lower_bound]) Returns the confidence interval for kurtosis. ci_mean([sig, meth

statsmodels.emplike.descriptive.DescStat statsmodels.emplike.descriptive.DescStat(endog) [source] Returns an instance to conduct inference on descriptive statistics via empirical likelihood. See DescStatUV and DescStatMV for more information. Parameters: endog : ndarray Array of data Returns : DescStat instance If k=1, the function returns a univariate instance, DescStatUV. If k>1, the function returns a multivariate instance, DescStatMV.

Empirical Likelihood emplike Introduction Empirical likelihood is a method of nonparametric inference and estimation that lifts the obligation of having to specify a family of underlying distributions. Moreover, empirical likelihood methods do not require re-sampling but still uniquely determine confidence regions whose shape mirrors the shape of the data. In essence, empirical likelihood attempts to combine the benefits of parametric and nonparametric methods while limiting their shortcomings

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.plot_forecast DynamicVAR.plot_forecast(steps=1, figsize=(10, 10)) [source] Plot h-step ahead forecasts against actual realizations of time series. Note that forecasts are lined up with their respective realizations. Parameters: steps : :

statsmodels.tsa.vector_ar.dynamic.DynamicVAR.forecast DynamicVAR.forecast(steps=1) [source] Produce dynamic forecast Parameters: steps : Returns: forecasts : pandas.DataFrame

statsmodels.duration.hazard_regression.PHRegResults class statsmodels.duration.hazard_regression.PHRegResults(model, params, cov_params, covariance_type='naive') [source] Class to contain results of fitting a Cox proportional hazards survival model. PHregResults inherits from statsmodels.LikelihoodModelResults Parameters: See statsmodels.LikelihoodModelResults : Returns: **Attributes** : model : class instance PHreg model instance that called fit. normalized_cov_params : array The samp