ANOVA Analysis of Variance models Examples In [1]: import statsmodels.api as sm In [2]: from statsmodels.formula.api import ols In [3]: moore = sm.datasets.get_rdataset("Moore", "car", ...: cache=True) # load data ...: In [4]: data = moore.data In [5]: data = data.rename(columns={"partner.status" : ...: "partner_status"}) # make name pythonic ...: In [6]: moore_lm = ols('conformity ~ C(fcategory, Sum)*C(partner_sta

statsmodels.robust.norms.AndrewWave.rho AndrewWave.rho(z) [source] The robust criterion function for Andrew?s wave. Parameters: z : array-like 1d array Returns: rho : array rho(z) = a*(1-cos(z/a)) for |z| <= a*pi rho(z) = 2*a for |z| > a*pi

statsmodels.robust.norms.AndrewWave.weights AndrewWave.weights(z) [source] Andrew?s wave weighting function for the IRLS algorithm The psi function scaled by z Parameters: z : array-like 1d array Returns: weights : array weights(z) = sin(z/a)/(z/a) for |z| <= a*pi weights(z) = 0 for |z| > a*pi

statsmodels.sandbox.distributions.extras.ACSkewT_gen.var ACSkewT_gen.var(*args, **kwds) Variance of the distribution Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: var : float the variance of the distribution

statsmodels.robust.norms.AndrewWave.psi_deriv AndrewWave.psi_deriv(z) [source] The derivative of Andrew?s wave psi function Notes Used to estimate the robust covariance matrix.

statsmodels.robust.norms.AndrewWave.psi AndrewWave.psi(z) [source] The psi function for Andrew?s wave The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = sin(z/a) for |z| <= a*pi psi(z) = 0 for |z| > a*pi

statsmodels.sandbox.distributions.extras.ACSkewT_gen.stats ACSkewT_gen.stats(*args, **kwds) Some statistics of the given RV Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional (discrete RVs only) scale parameter (default=1) moments : str, optional composed of letters [?mvsk?] defining which moments to

statsmodels.sandbox.distributions.extras.ACSkewT_gen.std ACSkewT_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: std : float standard deviation of the distribution

statsmodels.sandbox.distributions.extras.ACSkewT_gen.ppf ACSkewT_gen.ppf(q, *args, **kwds) Percent point function (inverse of cdf) at q of the given RV. Parameters: q : array_like lower tail probability arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: x : array_like quan

statsmodels.sandbox.distributions.extras.ACSkewT_gen.sf ACSkewT_gen.sf(x, *args, **kwds) Survival function (1-cdf) at x of the given RV. Parameters: x : array_like quantiles arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: sf : array_like Survival function evaluated at x