chi2testtable

vector with normalized data between 0 and 1.

alpha

flag, if <code>TRUE</code>, bin counts for a multi-variate contingency table are applied (Bagnato et al., 2012). Otherwise rules for univariate tables are used (Cochran, 1952; Mann and Wald, 1942).

(Contingency) tables for Pearson's Chi-square goodness-of-fit and independence tests.

Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.

chi2testtable: (Contingency) Tables for Chi-square Tests

Description

Usage

Arguments

Value

References