statsmodels.stats.diagnostic.CompareCox class statsmodels.stats.diagnostic.CompareCox 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 Formulas from Greene, section 8.3.4 translated to code : produces correct results for Example 8.3, Greene : Methods run(results_x, results_z[, attach]) run Cox test for non-nested models

statsmodels.iolib.foreign.StataReader.file_timestamp StataReader.file_timestamp() [source] Returns the date and time Stata recorded on last file save. Returns: out : str

statsmodels.stats.diagnostic.linear_lm statsmodels.stats.diagnostic.linear_lm(resid, exog, func=None) Lagrange multiplier test for linearity against functional alternative limitations: Assumes currently that the first column is integer. Currently it doesn?t check whether the transformed variables contain NaNs, for example log of negative number. Parameters: resid : ndarray residuals of a regression exog : ndarray exogenous variables for which linearity is tested func : callable If func

statsmodels.genmod.families.family.Poisson.starting_mu Poisson.starting_mu(y) Starting value for mu in the IRLS algorithm. Parameters: y : array The untransformed response variable. Returns: mu_0 : array The first guess on the transformed response variable. Notes Only the Binomial family takes a different initial value.

statsmodels.regression.mixed_linear_model.MixedLMResults.remove_data MixedLMResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an

statsmodels.genmod.families.family.NegativeBinomial.resid_dev NegativeBinomial.resid_dev(endog, mu, scale=1.0) [source] Negative Binomial Deviance Residual Parameters: endog : array-like endog is the response variable mu : array-like mu is the fitted value of the model scale : float, optional An optional argument to divide the residuals by scale Returns: resid_dev : array The array of deviance residuals Notes resid_dev = sign(endog-mu) * sqrt(piecewise) where piecewise is defined

statsmodels.tsa.arima_model.ARIMAResults.remove_data ARIMAResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribute is acce

statsmodels.miscmodels.count.PoissonGMLE.loglike PoissonGMLE.loglike(params)

statsmodels.discrete.discrete_model.LogitResults.llnull static LogitResults.llnull()

statsmodels.discrete.discrete_model.CountResults.bse static CountResults.bse()