statsmodels.sandbox.distributions.transformed.SquareFunc.derivplus SquareFunc.derivplus(x) [source]

statsmodels.iolib.table.SimpleTable.label_cells SimpleTable.label_cells(func) [source] Return None. Labels cells based on func. If func(cell) is None then its datatype is not changed; otherwise it is set to func(cell).

statsmodels.duration.hazard_regression.PHRegResults.pvalues static PHRegResults.pvalues()

statsmodels.genmod.generalized_estimating_equations.GEE.update_cached_means GEE.update_cached_means(mean_params) [source] cached_means should always contain the most recent calculation of the group-wise mean vectors. This function should be called every time the regression parameters are changed, to keep the cached means up to date.

statsmodels.nonparametric.kernel_density.KDEMultivariateConditional.cdf KDEMultivariateConditional.cdf(endog_predict=None, exog_predict=None) [source] Cumulative distribution function for the conditional density. Parameters: endog_predict: array_like, optional : The evaluation dependent variables at which the cdf is estimated. If not specified the training dependent variables are used. exog_predict: array_like, optional : The evaluation independent variables at which the cdf is estimated

statsmodels.stats.correlation_tools.cov_nearest statsmodels.stats.correlation_tools.cov_nearest(cov, method='clipped', threshold=1e-15, n_fact=100, return_all=False) [source] Find the nearest covariance matrix that is postive (semi-) definite This leaves the diagonal, i.e. the variance, unchanged Parameters: cov : ndarray, (k,k) initial covariance matrix method : string if ?clipped?, then the faster but less accurate corr_clipped is used. if ?nearest?, then corr_nearest is used threshol

Maximum Likelihood Estimation (Generic models) Link to Notebook GitHub This tutorial explains how to quickly implement new maximum likelihood models in statsmodels. We give two examples: Probit model for binary dependent variables Negative binomial model for count data The GenericLikelihoodModel class eases the process by providing tools such as automatic numeric differentiation and a unified interface to scipy optimization functions. Using statsmodels, users can fit new MLE models simply

statsmodels.discrete.discrete_model.NegativeBinomialResults.initialize NegativeBinomialResults.initialize(model, params, **kwd)

statsmodels.regression.linear_model.RegressionResults.conf_int RegressionResults.conf_int(alpha=0.05, cols=None) [source] Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The alpha level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return Notes The confidence interval is based on Student?s t-distribution.

statsmodels.regression.quantile_regression.QuantRegResults.save QuantRegResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. No