statsmodels.sandbox.regression.gmm.IVRegressionResults.mse_resid static IVRegressionResults.mse_resid()

statsmodels.regression.linear_model.RegressionResults.cov_params RegressionResults.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 : ar

statsmodels.tsa.arima_process.arma_impulse_response statsmodels.tsa.arima_process.arma_impulse_response(ar, ma, nobs=100) [source] get the impulse response function (MA representation) for ARMA process Parameters: ma : array_like, 1d moving average lag polynomial ar : array_like, 1d auto regressive lag polynomial nobs : int number of observations to calculate Returns: ir : array, 1d impulse response function with nobs elements Notes This is the same as finding the MA representati

statsmodels.sandbox.regression.gmm.IVRegressionResults.fvalue static IVRegressionResults.fvalue() [source]

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.predict NonlinearIVGMM.predict(params, exog=None) [source]

statsmodels.sandbox.distributions.transformed.absnormalg statsmodels.sandbox.distributions.transformed.absnormalg = Distribution based on a non-monotonic (u- or hump-shaped transformation) the constructor can be called with a distribution class, and functions that define the non-linear transformation. and generates the distribution of the transformed random variable Note: the transformation, it?s inverse and derivatives need to be fully specified: func, funcinvplus, funcinvminus, derivplus,

statsmodels.stats.power.TTestPower.plot_power TTestPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha, then one cur

statsmodels.discrete.discrete_model.MultinomialResults.conf_int MultinomialResults.conf_int(alpha=0.05, cols=None) [source]

statsmodels.stats.sandwich_covariance.cov_white_simple statsmodels.stats.sandwich_covariance.cov_white_simple(results, use_correction=True) [source] heteroscedasticity robust covariance matrix (White) Parameters: results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead Returns: cov : ndarray, (k_vars, k_vars) heteroscedasticity robust covariance matrix for parameter estimates See also cov_hc1, cov_hc2, cov_hc3 N

statsmodels.discrete.discrete_model.MultinomialResults.summary MultinomialResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) 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