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),

statsmodels.graphics.gofplots.ProbPlot.ppplot ProbPlot.ppplot(xlabel=None, ylabel=None, line=None, other=None, ax=None, **plotkwargs) [source] P-P plot of the percentiles (probabilities) of x versus the probabilities (percetiles) of a distribution. 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, optional Options f

statsmodels.discrete.discrete_model.ProbitResults.wald_test ProbitResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A

statsmodels.discrete.discrete_model.ProbitResults.t_test ProbitResults.t_test(r_matrix, cov_p=None, scale=None, use_t=None) Compute a t-test for a each linear hypothesis of the form Rb = q Parameters: r_matrix : array-like, str, tuple array : If an array is given, a p x k 2d array or length k 1d array specifying the linear restrictions. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple o

statsmodels.discrete.discrete_model.ProbitResults.summary2 ProbitResults.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 a

statsmodels.discrete.discrete_model.ProbitResults.summary ProbitResults.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 intervals Returns

statsmodels.discrete.discrete_model.ProbitResults.save ProbitResults.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 remo

statsmodels.discrete.discrete_model.ProbitResults.remove_data ProbitResults.remove_data() remove data arrays, all nobs arrays from result and model This reduces the size of the instance, so it can be pickled with less memory. Currently tested for use with predict from an unpickled results and model instance. Warning Since data and some intermediate results have been removed calculating new statistics that require them will raise exceptions. The exception will occur the first time an attribu

statsmodels.discrete.discrete_model.ProbitResults.pred_table ProbitResults.pred_table(threshold=0.5) Prediction table Parameters: threshold : scalar Number between 0 and 1. Threshold above which a prediction is considered 1 and below which a prediction is considered 0. Notes pred_table[i,j] refers to the number of times ?i? was observed and the model predicted ?j?. Correct predictions are along the diagonal.

statsmodels.discrete.discrete_model.ProbitResults.predict ProbitResults.predict(exog=None, transform=True, *args, **kwargs) Call self.model.predict with self.params as the first argument. Parameters: exog : array-like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data