statsmodels.discrete.discrete_model.LogitResults.cov_params LogitResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like,

statsmodels.discrete.discrete_model.LogitResults.conf_int LogitResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance 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 method : string Not Implemented Yet Method to estimate the confidence_interval.

statsmodels.discrete.discrete_model.Logit.score_obs Logit.score_obs(params) [source] Logit model Jacobian of the log-likelihood for each observation Parameters: params: array-like : The parameters of the model Returns: jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at params. Notes for observations

statsmodels.discrete.discrete_model.Logit.score Logit.score(params) [source] Logit model score (gradient) vector of the log-likelihood Parameters: params: array-like : The parameters of the model Returns: score : ndarray, 1-D The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at params Notes

statsmodels.discrete.discrete_model.Logit.predict Logit.predict(params, exog=None, linear=False) Predict response variable of a model given exogenous variables. Parameters: params : array-like Fitted parameters of the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. linear : bool, optional If True, returns the linear predictor dot(exog,params). Else, returns the value of the cdf at the linear predictor. Retur

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.discrete.discrete_model.Logit.loglikeobs Logit.loglikeobs(params) [source] Log-likelihood of logit model for each observation. Parameters: params : array-like The parameters of the logit model. Returns: loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at params. See Notes Notes for observations where . This simplification comes from the fact that the logistic distribution is symmetric.

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.discrete.discrete_model.Logit.jac Logit.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7 Logit model Jacobian of the log-likelihood for each observation Parameters: params: array-like : The parameters of the model Returns: jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at params. Notes for observations

statsmodels.genmod.families.links.Logit.inverse_deriv Logit.inverse_deriv(z) [source] Derivative of the inverse of the logit transform Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the logit function :