statsmodels.sandbox.tsa.fftarma.ArmaFft.invertroots ArmaFft.invertroots(retnew=False) make MA polynomial invertible by inverting roots inside unit circle Parameters: retnew : boolean If False (default), then return the lag-polynomial as array. If True, then return a new instance with invertible MA-polynomial Returns: manew : array new invertible MA lag-polynomial, returned if retnew is false. wasinvertible : boolean True if the MA lag-polynomial was already invertible, returned if re

statsmodels.discrete.discrete_model.Logit.loglike Logit.loglike(params) [source] Log-likelihood of logit model. Parameters: params : array-like The parameters of the logit model. Returns: loglike : float The log-likelihood function of the model evaluated at params. See notes. Notes Where . This simplification comes from the fact that the logistic distribution is symmetric.

statsmodels.sandbox.regression.gmm.GMMResults.save GMMResults.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. Notes If remove_data

statsmodels.stats.sandwich_covariance.cov_hac statsmodels.stats.sandwich_covariance.cov_hac(results, nlags=None, weights_func=, use_correction=True) heteroscedasticity and autocorrelation robust covariance matrix (Newey-West) Assumes we have a single time series with zero axis consecutive, equal spaced time periods Parameters: results : result instance result of a regression, uses results.model.exog and results.resid TODO: this should use wexog instead nlags : int or None highest lag to

statsmodels.genmod.families.family.NegativeBinomial.starting_mu NegativeBinomial.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.discrete.discrete_model.Poisson.fit_constrained Poisson.fit_constrained(constraints, start_params=None, **fit_kwds) [source] fit the model subject to linear equality constraints The constraints are of the form R params = q where R is the constraint_matrix and q is the vector of constraint_values. The estimation creates a new model with transformed design matrix, exog, and converts the results back to the original parameterization. Parameters: constraints : formula expression or

statsmodels.genmod.families.family.Poisson.loglike Poisson.loglike(endog, mu, scale=1.0) [source] Loglikelihood function for Poisson exponential family distribution. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional The default is 1. Returns: llf : float The value of the loglikelihood function evaluated at (endog,mu,scale) as defined below. Notes llf = scale * sum(-mu + endog*log(mu) - gammaln(endog+1

statsmodels.genmod.families.links.identity.deriv identity.deriv(p) Derivative of the power transform Parameters: p : array-like Mean parameters Returns: g?(p) : array Derivative of power transform of p Notes g?(p) = power * p`**(`power - 1)

statsmodels.stats.diagnostic.breaks_cusumolsresid statsmodels.stats.diagnostic.breaks_cusumolsresid(olsresidual, ddof=0) cusum test for parameter stability based on ols residuals Parameters: olsresiduals : ndarray array of residuals from an OLS estimation ddof : int number of parameters in the OLS estimation, used as degrees of freedom correction for error variance. Returns: sup_b : float test statistic, maximum of absolute value of scaled cumulative OLS residuals pval : float Prob

statsmodels.regression.mixed_linear_model.MixedLMResults.tvalues static MixedLMResults.tvalues() Return the t-statistic for a given parameter estimate.