statsmodels.regression.linear_model.RegressionResults.fittedvalues static RegressionResults.fittedvalues() [source]

statsmodels.genmod.families.family.InverseGaussian.deviance InverseGaussian.deviance(endog, mu, scale=1.0) [source] Inverse Gaussian deviance function Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float Deviance function as defined below Notes deviance = sum((endog=mu)**2/(endog*mu**2))

statsmodels.tsa.varma_process.VarmaPoly.vstackarma_minus1 VarmaPoly.vstackarma_minus1() [source] stack ar and lagpolynomial vertically in 2d array

statsmodels.stats.diagnostic.linear_rainbow statsmodels.stats.diagnostic.linear_rainbow(res, frac=0.5) Rainbow test for linearity The Null hypothesis is that the regression is correctly modelled as linear. The alternative for which the power might be large are convex, check Parameters: res : Result instance Returns: fstat : float test statistic based of F test pvalue : float pvalue of the test

statsmodels.sandbox.stats.multicomp.MultiComparison.tukeyhsd MultiComparison.tukeyhsd(alpha=0.05) [source] Tukey?s range test to compare means of all pairs of groups Parameters: alpha : float, optional Value of FWER at which to calculate HSD. Returns: results : TukeyHSDResults instance A results class containing relevant data and some post-hoc calculations

statsmodels.sandbox.tsa.fftarma.ArmaFft.acovf ArmaFft.acovf(nobs=None) theoretical autocovariance function of ARMA process Parameters: ar : array_like, 1d coefficient for autoregressive lag polynomial, including zero lag ma : array_like, 1d coefficient for moving-average lag polynomial, including zero lag nobs : int number of terms (lags plus zero lag) to include in returned acovf Returns: acovf : array autocovariance of ARMA process given by ar, ma See also arma_acf, acovf Not

statsmodels.stats.power.tt_solve_power statsmodels.stats.power.tt_solve_power = > solve for any one parameter of the power of a one sample t-test for the one sample t-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. This test can also be used for a paired t-test, where effect size is defined in terms of the mean difference, and nobs is the number of pairs. Parameters: effect_size : float standardized effect size, mean d

statsmodels.genmod.generalized_estimating_equations.GEE class statsmodels.genmod.generalized_estimating_equations.GEE(endog, exog, groups, time=None, family=None, cov_struct=None, missing='none', offset=None, exposure=None, dep_data=None, constraint=None, update_dep=True, **kwargs) [source] Estimation of marginal regression models using Generalized Estimating Equations (GEE). GEE can be used to fit Generalized Linear Models (GLMs) when the data have a grouped structure, and the observations

statsmodels.regression.mixed_linear_model.MixedLMResults.bse static MixedLMResults.bse()

statsmodels.robust.norms.TrimmedMean.psi_deriv TrimmedMean.psi_deriv(z) [source] The derivative of least trimmed mean psi function Notes Used to estimate the robust covariance matrix.