statsmodels.sandbox.stats.multicomp.MultiComparison class statsmodels.sandbox.stats.multicomp.MultiComparison(data, groups, group_order=None) [source] Tests for multiple comparisons Parameters: data : array independent data samples groups : array group labels corresponding to each data point group_order : list of strings, optional the desired order for the group mean results to be reported in. If not specified, results are reported in increasing order. If group_order does not contain a

statsmodels.stats.weightstats.ttost_paired statsmodels.stats.weightstats.ttost_paired(x1, x2, low, upp, transform=None, weights=None) [source] test of (non-)equivalence for two dependent, paired sample TOST: two one-sided t tests null hypothesis: md < low or md > upp alternative hypothesis: low < md < upp where md is the mean, expected value of the difference x1 - x2 If the pvalue is smaller than a threshold,say 0.05, then we reject the hypothesis that the difference between the

statsmodels.discrete.discrete_model.LogitResults.predict LogitResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data st

statsmodels.discrete.discrete_model.ProbitResults.summary ProbitResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns

statsmodels.sandbox.regression.gmm.IV2SLS.hessian IV2SLS.hessian(params) The Hessian matrix of the model

statsmodels.genmod.cov_struct.Independence.covariance_matrix Independence.covariance_matrix(expval, index) [source] Returns the working covariance or correlation matrix for a given cluster of data. Parameters: endog_expval: array-like : The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer : The index of the cluster for which the covariane or correlation matrix will be returned Returns: M: matrix : The covariance o

Input-Output iolib statsmodels offers some functions for input and output. These include a reader for STATA files, a class for generating tables for printing in several formats and two helper functions for pickling. Users can also leverage the powerful input/output functions provided by pandas.io. Among other things, pandas (a statsmodels dependency) allows reading and writing to Excel, CSV, and HDF5 (PyTables). Examples SimpleTable: Basic example Module Reference foreign.StataReader(f

statsmodels.miscmodels.count.PoissonZiGMLE.nloglikeobs PoissonZiGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes

statsmodels.genmod.families.family.Gaussian.fitted Gaussian.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.duration.hazard_regression.PHRegResults.schoenfeld_residuals static PHRegResults.schoenfeld_residuals() [source] A matrix containing the Schoenfeld residuals. Notes Schoenfeld residuals for censored observations are set to zero.