Models for Survival and Duration Analysis Examples import statsmodels.api as sm import statsmodels.formula.api as smf data = sm.datasets.get_rdataset("flchain", "survival").data del data["chapter"] data = data.dropna() data["lam"] = data["lambda"] data["female"] = (data["sex"] == "F").astype(int) data["year"] = data["sample.yr"] - min(data["sample.yr"]) status = data["death"].values mod = smf.phreg("futime ~ 0 + age + female + creatinine + " "np.sqrt(kappa) + np.sqrt(lam) + y

statsmodels.discrete.discrete_model.MNLogit.score MNLogit.score(params) [source] Score matrix for multinomial logit model log-likelihood Parameters: params : array The parameters of the multinomial logit model. Returns: score : ndarray, (K * (J-1),) The 2-d score vector, i.e. the first derivative of the loglikelihood function, of the multinomial logit model evaluated at params. Notes for In the multinomial model the score matrix is K x J-1 but is returned as a flattened array to

statsmodels.discrete.discrete_model.MNLogit.predict MNLogit.predict(params, exog=None, linear=False) Predict response variable of a model given exogenous variables. Parameters: params : array-like 2d array of fitted parameters of the model. Should be in the order returned from the model. exog : array-like 1d or 2d array of exogenous values. If not supplied, the whole exog attribute of the model is used. If a 1d array is given it assumed to be 1 row of exogenous variables. If you only hav

statsmodels.discrete.discrete_model.MNLogit.pdf MNLogit.pdf(eXB) [source] NotImplemented

statsmodels.discrete.discrete_model.MNLogit.loglikeobs MNLogit.loglikeobs(params) [source] Log-likelihood of the multinomial logit model for each observation. Parameters: params : array-like The parameters of the multinomial logit model. Returns: loglike : ndarray (nobs,) The log likelihood for each observation of the model evaluated at params. See Notes Notes for observations where if individual i chose alternative j and 0 if not.

statsmodels.discrete.discrete_model.MNLogit.loglike_and_score MNLogit.loglike_and_score(params) [source] Returns log likelihood and score, efficiently reusing calculations. Note that both of these returned quantities will need to be negated before being minimized by the maximum likelihood fitting machinery.

statsmodels.discrete.discrete_model.MNLogit.loglike MNLogit.loglike(params) [source] Log-likelihood of the multinomial logit model. Parameters: params : array-like The parameters of the multinomial logit model. Returns: loglike : float The log-likelihood function of the model evaluated at params. See notes. Notes where if individual i chose alternative j and 0 if not.

statsmodels.discrete.discrete_model.MNLogit.jac MNLogit.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7 Jacobian matrix for multinomial logit model log-likelihood Parameters: params : array The parameters of the multinomial logit model. Returns: jac : ndarray, (nobs, k_vars*(J-1)) The derivative of the loglikelihood for each observation evaluated at params . Notes for , for observations In the multinomial model the sc

statsmodels.discrete.discrete_model.MNLogit.initialize MNLogit.initialize() Preprocesses the data for MNLogit.

statsmodels.discrete.discrete_model.MNLogit.from_formula classmethod MNLogit.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataFrame args :