statsmodels.sandbox.distributions.extras.NormExpan_gen.median NormExpan_gen.median(*args, **kwds) Median of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional Location parameter, Default is 0. scale : array_like, optional Scale parameter, Default is 1. Returns: median : float The median of the distribution. See also stats.distributions.

statsmodels.sandbox.regression.gmm.LinearIVGMM.from_formula classmethod LinearIVGMM.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame

statsmodels.sandbox.regression.gmm.IV2SLS.predict IV2SLS.predict(params, exog=None) [source] Return linear predicted values from a design matrix. Parameters: exog : array-like Design / exogenous data params : array-like, optional after fit has been called Parameters of a linear model Returns: An array of fitted values : Notes If the model as not yet been fit, params is not optional.

statsmodels.tsa.vector_ar.var_model.VAR.fit VAR.fit(maxlags=None, method='ols', ic=None, trend='c', verbose=False) [source] Fit the VAR model Parameters: maxlags : int Maximum number of lags to check for order selection, defaults to 12 * (nobs/100.)**(1./4), see select_order function method : {?ols?} Estimation method to use ic : {?aic?, ?fpe?, ?hqic?, ?bic?, None} Information criterion to use for VAR order selection. aic : Akaike fpe : Final prediction error hqic : Hannan-Quinn bic :

statsmodels.discrete.discrete_model.DiscreteResults.conf_int DiscreteResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_int

statsmodels.tsa.vector_ar.irf.IRAnalysis.errband_mc IRAnalysis.errband_mc(orth=False, svar=False, repl=1000, signif=0.05, seed=None, burn=100) [source] IRF Monte Carlo integrated error bands

statsmodels.sandbox.distributions.extras.SkewNorm_gen.ppf SkewNorm_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: x : array_like qu

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.duration.hazard_regression.PHReg.fit PHReg.fit(groups=None, **args) [source] Fit a proportional hazards regression model. Parameters: groups : array-like Labels indicating groups of observations that may be dependent. If present, the standard errors account for this dependence. Does not affect fitted values. Returns a PHregResults instance. :

statsmodels.sandbox.distributions.transformed.LogTransf_gen.cdf LogTransf_gen.cdf(x, *args, **kwds) Cumulative distribution function of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: cdf : ndarray Cumulative distributi