statsmodels.stats.correlation_tools.corr_nearest statsmodels.stats.correlation_tools.corr_nearest(corr, threshold=1e-15, n_fact=100) [source] Find the nearest correlation matrix that is positive semi-definite. The function iteratively adjust the correlation matrix by clipping the eigenvalues of a difference matrix. The diagonal elements are set to one. Parameters: corr : ndarray, (k, k) initial correlation matrix threshold : float clipping threshold for smallest eigenvalue, see Notes n_

statsmodels.stats.descriptivestats.sign_test statsmodels.stats.descriptivestats.sign_test(samp, mu0=0) [source] Signs test. Parameters: samp : array-like 1d array. The sample for which you want to perform the signs test. mu0 : float See Notes for the definition of the sign test. mu0 is 0 by default, but it is common to set it to the median. Returns: M, p-value : See also scipy.stats.wilcoxon Notes The signs test returns M = (N(+) - N(-))/2 where N(+) is the number of values above m

statsmodels.stats.anova.anova_lm statsmodels.stats.anova.anova_lm(*args, **kwargs) [source] ANOVA table for one or more fitted linear models. Parameters: args : fitted linear model results instance One or more fitted linear models scale : float Estimate of variance, If None, will be estimated from the largest model. Default is None. test : str {?F?, ?Chisq?, ?Cp?} or None Test statistics to provide. Default is ?F?. typ : str or int {?I?,?II?,?III?} or {1,2,3} The type of ANOVA test t

statsmodels.stats.correlation_tools.corr_clipped statsmodels.stats.correlation_tools.corr_clipped(corr, threshold=1e-15) [source] Find a near correlation matrix that is positive semi-definite This function clips the eigenvalues, replacing eigenvalues smaller than the threshold by the threshold. The new matrix is normalized, so that the diagonal elements are one. Compared to corr_nearest, the distance between the original correlation matrix and the positive definite correlation matrix is larg

Statistics stats This section collects various statistical tests and tools. Some can be used independently of any models, some are intended as extension to the models and model results. API Warning: The functions and objects in this category are spread out in various modules and might still be moved around. We expect that in future the statistical tests will return class instances with more informative reporting instead of only the raw numbers. Residual Diagnostics and Specification Tests dur

statsmodels.tsa.vector_ar.var_model.VARResults.tvalues static VARResults.tvalues() [source] Compute t-statistics. Use Student-t(T - Kp - 1) = t(df_resid) to test significance.

statsmodels.tsa.vector_ar.var_model.VARResults.roots static VARResults.roots() [source]

statsmodels.tsa.vector_ar.var_model.VARResults.sigma_u_mle static VARResults.sigma_u_mle() [source] (Biased) maximum likelihood estimate of noise process covariance

statsmodels.tsa.vector_ar.var_model.VARResults.stderr static VARResults.stderr() [source] Standard errors of coefficients, reshaped to match in size

statsmodels.tsa.vector_ar.var_model.VARResults.resid_corr static VARResults.resid_corr() [source] Centered residual correlation matrix