CompareMeans.tconfint_diff()

statsmodels.stats.weightstats.CompareMeans.tconfint_diff CompareMeans.tconfint_diff(alpha=0.05, alternative='two-sided', usevar='pooled') [source] confidence interval for the difference in means Parameters: alpha : float significance level for the confidence interval, coverage is 1-alpha alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternative hypothesis, H1, has to be one of the following : ?two-sided?: H1:

CompareMeans.dof_satt()

statsmodels.stats.weightstats.CompareMeans.dof_satt CompareMeans.dof_satt() [source] degrees of freedom of Satterthwaite for unequal variance

CompareJ.run()

statsmodels.stats.diagnostic.CompareJ.run CompareJ.run(results_x, results_z, attach=True) run J-test for non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool If true, then the intermediate results are attached to the instance. Returns: tstat : float t statistic for the test that including the fitted values of the first model in the second model has no effect. pvalue : floa

CompareCox.run()

statsmodels.stats.diagnostic.CompareCox.run CompareCox.run(results_x, results_z, attach=True) run Cox test for non-nested models Parameters: results_x : Result instance result instance of first model results_z : Result instance result instance of second model attach : bool If true, then the intermediate results are attached to the instance. Returns: tstat : float t statistic for the test that including the fitted values of the first model in the second model has no effect. pvalue

CLogLog.inverse_deriv()

statsmodels.genmod.families.links.CLogLog.inverse_deriv CLogLog.inverse_deriv(z) [source] Derivative of the inverse of the C-Log-Log transform link function Parameters: z : array-like The value of the inverse of the CLogLog link function at p Returns: The derivative of the inverse of the CLogLog link function :

CLogLog.inverse()

statsmodels.genmod.families.links.CLogLog.inverse CLogLog.inverse(z) [source] Inverse of C-Log-Log transform link function Parameters: z : array-like The value of the inverse of the CLogLog link function at p Returns: p : array Mean parameters Notes g^(-1)(z) = 1-exp(-exp(z))

CLogLog.deriv2()

statsmodels.genmod.families.links.CLogLog.deriv2 CLogLog.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

CLogLog.deriv()

statsmodels.genmod.families.links.CLogLog.deriv CLogLog.deriv(p) [source] Derivative of C-Log-Log transform link function Parameters: p : array-like Mean parameters Returns: g?(p) : array The derivative of the CLogLog transform link function Notes g?(p) = - 1 / (log(p) * p)

CDFLink.inverse_deriv()

statsmodels.genmod.families.links.CDFLink.inverse_deriv CDFLink.inverse_deriv(z) [source] Derivative of the inverse of the CDF transformation link function Parameters: z : array The inverse of the link function at p Returns: The value of the derivative of the inverse of the logit function :

CDFLink.inverse()

statsmodels.genmod.families.links.CDFLink.inverse CDFLink.inverse(z) [source] The inverse of the CDF link Parameters: z : array-like The value of the inverse of the link function at p Returns: p : array Mean probabilities. The value of the inverse of CDF link of z Notes g^(-1)(z) = dbn.cdf(z)