statsmodels.sandbox.distributions.extras.NormExpan_gen.interval NormExpan_gen.interval(alpha, *args, **kwds) Confidence interval with equal areas around the median. Parameters: alpha : array_like of float Probability that an rv will be drawn from the returned range. Each value should be in the range [0, 1]. arg1, arg2, ... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional location parameter,

statsmodels.emplike.descriptive.DescStatUV.plot_contour DescStatUV.plot_contour(mu_low, mu_high, var_low, var_high, mu_step, var_step, levs=[0.2, 0.1, 0.05, 0.01, 0.001]) [source] Returns a plot of the confidence region for a univariate mean and variance. Parameters: mu_low : float Lowest value of the mean to plot mu_high : float Highest value of the mean to plot var_low : float Lowest value of the variance to plot var_high : float Highest value of the variance to plot mu_step : flo

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

statsmodels.robust.scale.HuberScale class statsmodels.robust.scale.HuberScale(d=2.5, tol=1e-08, maxiter=30) [source] Huber?s scaling for fitting robust linear models. Huber?s scale is intended to be used as the scale estimate in the IRLS algorithm and is slightly different than the Huber class. Parameters: d : float, optional d is the tuning constant for Huber?s scale. Default is 2.5 tol : float, optional The convergence tolerance maxiter : int, optiona The maximum number of iterations

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

statsmodels.genmod.families.family.Gaussian.starting_mu Gaussian.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

statsmodels.regression.linear_model.GLSAR.predict GLSAR.predict(params, exog=None) Return linear predicted values from a design matrix. Parameters: params : array-like Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model has not yet been fit, params is not optional.

statsmodels.tools.eval_measures.mse statsmodels.tools.eval_measures.mse(x1, x2, axis=0) [source] mean squared error Parameters: x1, x2 : array_like The performance measure depends on the difference between these two arrays. axis : int axis along which the summary statistic is calculated Returns: mse : ndarray or float mean squared error along given axis. Notes If x1 and x2 have different shapes, then they need to broadcast. This uses numpy.asanyarray to convert the input. Whether t

statsmodels.iolib.summary2.Summary.add_array Summary.add_array(array, align='r', float_format='%.4f') [source] Add the contents of a Numpy array to summary table Parameters: array : numpy array (2D) float_format: string : Formatting to array if type is float align : string Data alignment (l/c/r)

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. :