statsmodels.regression.quantile_regression.QuantReg.predict QuantReg.predict(params, exog=None) Return linear predicted values from a design matrix. Parameters: params : array-like Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model has not yet been fit, params is not optional.

statsmodels.regression.quantile_regression.QuantReg.loglike QuantReg.loglike(params) Log-likelihood of model.

statsmodels.regression.quantile_regression.QuantReg.initialize QuantReg.initialize()

statsmodels.regression.quantile_regression.QuantReg.information QuantReg.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.regression.quantile_regression.QuantReg.hessian QuantReg.hessian(params) The Hessian matrix of the model

statsmodels.regression.quantile_regression.QuantReg.from_formula classmethod QuantReg.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.DataFram

statsmodels.regression.quantile_regression.QuantReg.fit_regularized QuantReg.fit_regularized(method='coord_descent', maxiter=1000, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-08, zero_tol=1e-08, **kwargs) Return a regularized fit to a linear regression model. Parameters: method : string Only the coordinate descent algorithm is implemented. maxiter : integer The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables). alpha :

statsmodels.regression.quantile_regression.QuantReg.fit QuantReg.fit(q=0.5, vcov='robust', kernel='epa', bandwidth='hsheather', max_iter=1000, p_tol=1e-06, **kwargs) [source] Solve by Iterative Weighted Least Squares Parameters: q : float Quantile must be between 0 and 1 vcov : string, method used to calculate the variance-covariance matrix of the parameters. Default is robust: robust : heteroskedasticity robust standard errors (as suggested in Greene 6th edition) iid : iid errors (as in

Quantile regression Link to Notebook GitHub This example page shows how to use statsmodels' QuantReg class to replicate parts of the analysis published in Koenker, Roger and Kevin F. Hallock. "Quantile Regressioin". Journal of Economic Perspectives, Volume 15, Number 4, Fall 2001, Pages 143?156 We are interested in the relationship between income and expenditures on food for a sample of working class Belgian households in 1857 (the Engel data). Setup We first need to load some modules and

statsmodels.graphics.gofplots.ProbPlot.qqplot ProbPlot.qqplot(xlabel=None, ylabel=None, line=None, other=None, ax=None, **plotkwargs) [source] Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution or the quantiles of another ProbPlot instance. Parameters: xlabel, ylabel : str or None, optional User-provided lables for the x-axis and y-axis. If None (default), other values are used depending on the status of the kwarg other. line : str {?45?, ?s?, ?r?, q?} or None, opti