statsmodels.regression.quantile_regression.QuantReg.initialize QuantReg.initialize()

statsmodels.tsa.arima_process.ar2arma statsmodels.tsa.arima_process.ar2arma(ar_des, p, q, n=20, mse='ar', start=None) [source] find arma approximation to ar process This finds the ARMA(p,q) coefficients that minimize the integrated squared difference between the impulse_response functions (MA representation) of the AR and the ARMA process. This does currently not check whether the MA lagpolynomial of the ARMA process is invertible, neither does it check the roots of the AR lagpolynomial. Par

statsmodels.tsa.arima_process.arma2ar statsmodels.tsa.arima_process.arma2ar(ar, ma, nobs=100) [source] get the AR representation of an ARMA process Parameters: ar : array_like, 1d auto regressive lag polynomial ma : array_like, 1d moving average lag polynomial nobs : int number of observations to calculate Returns: ar : array, 1d coefficients of AR lag polynomial with nobs elements ` : Notes This is just an alias for ar_representation = arma_impulse_response(ma, ar, nobs=100) ful

statsmodels.regression.quantile_regression.QuantRegResults.cov_HC0 static QuantRegResults.cov_HC0() See statsmodels.RegressionResults

Interactions and ANOVA Link to Notebook GitHub Note: This script is based heavily on Jonathan Taylor's class notes http://www.stanford.edu/class/stats191/interactions.html Download and format data: In [1]: from __future__ import print_function from statsmodels.compat import urlopen import numpy as np np.set_printoptions(precision=4, suppress=True) import statsmodels.api as sm import pandas as pd pd.set_option("display.width", 100) import matplotlib.pyplot as plt from statsmodels.fo

statsmodels.stats.weightstats._tconfint_generic statsmodels.stats.weightstats._tconfint_generic(mean, std_mean, dof, alpha, alternative) [source] generic t-confint to save typing

statsmodels.discrete.discrete_model.BinaryResults.fittedvalues static BinaryResults.fittedvalues()

statsmodels.stats.power.GofChisquarePower class statsmodels.stats.power.GofChisquarePower(**kwds) [source] Statistical Power calculations for one sample chisquare test 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, n_bins[, ddof]) Calculate the power of a chisquare test for one sample solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a one sample ch

statsmodels.sandbox.stats.multicomp.tiecorrect statsmodels.sandbox.stats.multicomp.tiecorrect(xranks) [source] should be equivalent of scipy.stats.tiecorrect

statsmodels.stats.diagnostic.compare_j statsmodels.stats.diagnostic.compare_j = J-Test for comparing non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool From description in Greene, section 8.3.3 : produces correct results for Example 8.3, Greene - not checked yet : #currently an exception, but I don?t have clean reload in python session : check what results should be attached