statsmodels.miscmodels.count.PoissonOffsetGMLE.nloglike PoissonOffsetGMLE.nloglike(params)

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

statsmodels.stats.power.FTestAnovaPower class statsmodels.stats.power.FTestAnovaPower(**kwds) [source] Statistical Power calculations F-test for one factor balanced ANOVA 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[, k_groups]) Calculate the power of a F-test for one factor ANOVA. solve_power([effect_size, nobs, alpha, ...]) solve for any one parameter of the power of a F-test

statsmodels.tsa.stattools.periodogram statsmodels.tsa.stattools.periodogram(X) [source] Returns the periodogram for the natural frequency of X Parameters: X : array-like Array for which the periodogram is desired. Returns: pgram : array 1./len(X) * np.abs(np.fft.fft(X))**2 References Brockwell and Davis.

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter.loglike classmethod KalmanFilter.loglike(params, arma_model, set_sigma2=True) [source] The loglikelihood for an ARMA model using the Kalman Filter recursions. Parameters: params : array The coefficients of the ARMA model, assumed to be in the order of trend variables and k exogenous coefficients, the p AR coefficients, then the q MA coefficients. arma_model : statsmodels.tsa.arima.ARMA instance A reference to the ARMA model instance. set

statsmodels.genmod.families.family.InverseGaussian.resid_anscombe InverseGaussian.resid_anscombe(endog, mu) [source] The Anscombe residuals for the inverse Gaussian distribution Parameters: endog : array Endogenous response variable mu : array Fitted mean response variable Returns: resid_anscombe : array The Anscombe residuals for the inverse Gaussian distribution as defined below Notes resid_anscombe = log(endog/mu)/sqrt(mu)

statsmodels.stats.weightstats.DescrStatsW.mean static DescrStatsW.mean() [source] weighted mean of data

statsmodels.stats.weightstats.CompareMeans.ttest_ind CompareMeans.ttest_ind(alternative='two-sided', usevar='pooled', value=0) [source] ttest for the null hypothesis of identical means this should also be the same as onewaygls, except for ddof differences Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following ?two-sided?: H1: difference in means not equal to value (def

statsmodels.stats.diagnostic.het_white statsmodels.stats.diagnostic.het_white(resid, exog, retres=False) White?s Lagrange Multiplier Test for Heteroscedasticity Parameters: resid : array_like residuals, square of it is used as endogenous variable exog : array_like possible explanatory variables for variance, squares and interaction terms are included in the auxilliary regression. resstore : instance (optional) a class instance that holds intermediate results. Only returned if store=Tru

statsmodels.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle