\[\DeclareMathOperator{\erf}{erf} \DeclareMathOperator{\argmin}{argmin} \newcommand{\R}{\mathbb{R}} \newcommand{\n}{\boldsymbol{n}}\]

Module pyqt_fit.bootstrap

Bootstrap Shuffling Methods

Main Boostrap Functions

class pyqt_fit.bootstrap.BootstrapResult(y_fit, y_est, y_eval, CIs, shuffled_xs, shuffled_ys, full_results)

Note

This is a class created with pyqt_fit.utils.namedtuple().

y_fit

Y estimated on xdata

y_est: ndarray

Y estimated on eval_points

CIs

List of confidence intervals. The first element is for the estimated values on eval_points. The others are for the extra attributes specified in extra_attrs. Each array is a 3-dimensional array (Q,2,N), where Q is the number of confidence interval and N is the number of data points. Values (x,0,y) give the lower bounds and (x,1,y) the upper bounds of the confidence intervals.

shuffled_xs

if full_results is True, the shuffled x’s used for the bootstrapping

shuffled_ys

if full_results is True, the shuffled y’s used for the bootstrapping

full_results

if full_results is True, the estimated y’s for each shuffled_ys

Table Of Contents

Previous topic

Module pyqt_fit.curve_fitting

Next topic

Module pyqt_fit.kernel_smoothing

This Page