statsmodels.regression.quantile_regression.QuantReg.score QuantReg.score(params) Score vector of model. The gradient of logL with respect to each parameter.

statsmodels.discrete.discrete_model.Logit class statsmodels.discrete.discrete_model.Logit(endog, exog, **kwargs) [source] Binary choice logit model Parameters: endog : array-like 1-d endogenous response variable. The dependent variable. exog : array-like A nobs x k array where nobs is the number of observations and k is the number of regressors. An intercept is not included by default and should be added by the user. See statsmodels.tools.add_constant. missing : str Available options a

statsmodels.discrete.discrete_model.NegativeBinomialResults.summary NegativeBinomialResults.summary(yname=None, xname=None, title=None, alpha=0.05, yname_list=None) Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence

statsmodels.regression.linear_model.OLSResults.rsquared static OLSResults.rsquared()

statsmodels.discrete.discrete_model.LogitResults.summary2 LogitResults.summary2(yname=None, xname=None, title=None, alpha=0.05, float_format='%.4f') Experimental function to summarize regression results Parameters: xname : List of strings of length equal to the number of parameters Names of the independent variables (optional) yname : string Name of the dependent variable (optional) title : string, optional Title for the top table. If not None, then this replaces the default title alp

statsmodels.discrete.discrete_model.NegativeBinomialResults.bic static NegativeBinomialResults.bic() [source]

statsmodels.discrete.discrete_model.Probit.pdf Probit.pdf(X) [source] Probit (Normal) probability density function Parameters: X : array-like The linear predictor of the model (XB). Returns: pdf : ndarray The value of the normal density function for each point of X. Notes This function is just an alias for scipy.stats.norm.pdf

statsmodels.graphics.regressionplots.plot_regress_exog statsmodels.graphics.regressionplots.plot_regress_exog(results, exog_idx, fig=None) [source] Plot regression results against one regressor. This plots four graphs in a 2 by 2 figure: ?endog versus exog?, ?residuals versus exog?, ?fitted versus exog? and ?fitted plus residual versus exog? Parameters: results : result instance result instance with resid, model.endog and model.exog as attributes exog_idx : int index of regressor in exog

statsmodels.sandbox.regression.gmm.LinearIVGMM.from_formula classmethod LinearIVGMM.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

statsmodels.graphics.gofplots.ProbPlot.probplot ProbPlot.probplot(xlabel=None, ylabel=None, line=None, exceed=False, ax=None, **plotkwargs) [source] Probability plot of the unscaled quantiles of x versus the probabilities of a distibution (not to be confused with a P-P plot). The x-axis is scaled linearly with the quantiles, but the probabilities are used to label the axis. Parameters: xlabel, ylabel : str or None, optional User-provided lables for the x-axis and y-axis. If None (default),