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

statsmodels.robust.robust_linear_model.RLMResults.cov_params RLMResults.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 : array-like, o

statsmodels.tsa.vector_ar.var_model.VARResults.mean VARResults.mean() Mean of stable process Lutkepohl eq. 2.1.23

statsmodels.sandbox.regression.gmm.GMM.from_formula classmethod GMM.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 args : extra ar

statsmodels.sandbox.stats.runs.Runs.runs_test Runs.runs_test(correction=True) [source] basic version of runs test Parameters: correction: bool : Following the SAS manual, for samplesize below 50, the test statistic is corrected by 0.5. This can be turned off with correction=False, and was included to match R, tseries, which does not use any correction. pvalue based on normal distribution, with integer correction :

statsmodels.sandbox.distributions.transformed.squarenormalg statsmodels.sandbox.distributions.transformed.squarenormalg = 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, deri

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.emplike.descriptive.DescStatUV.test_var DescStatUV.test_var(sig2_0, return_weights=False) [source] Returns -2 x log-likelihoog ratio and the p-value for the hypothesized variance Parameters: sig2_0 : float Hypothesized variance to be tested return_weights : bool If True, returns the weights that maximize the likelihood of observing sig2_0. Default is False Returns: test_results : tuple The log-likelihood ratio and the p_value of sig2_0 Examples >>> random_numbe

statsmodels.emplike.descriptive.DescStatUV.ci_kurt DescStatUV.ci_kurt(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence interval for kurtosis. Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value of kurtosis the upper limit can be. Default is .99 confidence limit assuming normality. lower_bound : float Minimum value of kurtosis the lower limit can be. Default is .99 confidence limit assuming normality. Returns: