statsmodels.discrete.discrete_model.LogitResults.resid_response static LogitResults.resid_response() The response residuals Notes Response residuals are defined to be where .

statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd(y, x) [source]

statsmodels.genmod.generalized_estimating_equations.GEE.predict GEE.predict(params, exog=None, offset=None, exposure=None, linear=False) [source] Return predicted values for a marginal regression model fit using GEE. Parameters: params : array-like Parameters / coefficients of a marginal regression model. exog : array-like, optional Design / exogenous data. If exog is None, model exog is used. offset : array-like, optional Offset for exog if provided. If offset is None, model offset is

statsmodels.miscmodels.count.PoissonZiGMLE.expandparams PoissonZiGMLE.expandparams(params) expand to full parameter array when some parameters are fixed Parameters: params : array reduced parameter array Returns: paramsfull : array expanded parameter array where fixed parameters are included Notes Calling this requires that self.fixed_params and self.fixed_paramsmask are defined. developer notes: This can be used in the log-likelihood to ... this could also be replaced by a more gene

statsmodels.stats.gof.gof_chisquare_discrete statsmodels.stats.gof.gof_chisquare_discrete(distfn, arg, rvs, alpha, msg) [source] perform chisquare test for random sample of a discrete distribution Parameters: distname : string name of distribution function arg : sequence parameters of distribution alpha : float significance level, threshold for p-value Returns: result : bool 0 if test passes, 1 if test fails Notes originally written for scipy.stats test suite, still needs to be c

statsmodels.genmod.cov_struct.Autoregressive.covariance_matrix_solve Autoregressive.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each

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.discrete.discrete_model.Logit.pdf Logit.pdf(X) [source] The logistic probability density function Parameters: X : array-like X is the linear predictor of the logit model. See notes. Returns: pdf : ndarray The value of the Logit probability mass function, PMF, for each point of X. np.exp(-x)/(1+np.exp(-X))**2 Notes In the logit model,

statsmodels.tsa.ar_model.AR.score AR.score(params) [source] Return the gradient of the loglikelihood at params. Parameters: params : array-like The parameter values at which to evaluate the score function. Notes Returns numerical gradient.

statsmodels.robust.norms.RamsayE.psi RamsayE.psi(z) [source] The psi function for Ramsay?s Ea estimator The analytic derivative of rho Parameters: z : array-like 1d array Returns: psi : array psi(z) = z*exp(-a*|z|)