statsmodels.genmod.families.family.Gamma.predict Gamma.predict(mu) Linear predictors based on given mu values. Parameters: mu : array The mean response variables Returns: lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu.

statsmodels.genmod.families.family.Gamma.loglike Gamma.loglike(endog, mu, scale=1.0) [source] Loglikelihood function for Gamma exponential family distribution. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes llf = -1/scale * sum(endog/mu + log(mu) + (scale-1)*log(endog

statsmodels.genmod.families.family.Gamma.fitted Gamma.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.genmod.families.family.Gamma.deviance Gamma.deviance(endog, mu, scale=1.0) [source] Gamma 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 = 2*sum((endog - mu)/mu - log(endog/mu))

statsmodels.stats.power.FTestPower.solve_power FTestPower.solve_power(effect_size=None, df_num=None, df_denom=None, nobs=None, alpha=None, power=None, ncc=1) [source] solve for any one parameter of the power of a F-test for the one sample F-test the keywords are: effect_size, df_num, df_denom, alpha, power Exactly one needs to be None, all others need numeric values. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be posi

statsmodels.stats.power.FTestPower.power FTestPower.power(effect_size, df_num, df_denom, alpha, ncc=1) [source] Calculate the power of a F-test. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. df_num : int or float numerator degrees of freedom. df_denom : int or float denominator degrees of freedom. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that

statsmodels.stats.power.FTestPower.plot_power FTestPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha, then one cur

statsmodels.stats.power.FTestAnovaPower.solve_power FTestAnovaPower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, k_groups=2) [source] solve for any one parameter of the power of a F-test for the one sample F-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or floa

statsmodels.stats.power.FTestAnovaPower.power FTestAnovaPower.power(effect_size, nobs, alpha, k_groups=2) [source] Calculate the power of a F-test for one factor ANOVA. Parameters: effect_size : float standardized effect size, mean divided by the standard deviation. effect size has to be positive. nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the probability of a type I error, that is wrong rejections if the N

statsmodels.stats.power.FTestAnovaPower.plot_power FTestAnovaPower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha, th