HotellingEllipse - Hotelling’s T-Squared Statistic and Ellipse

Functions to calculate the Hotelling’s T-squared statistic and corresponding confidence ellipses. Provides the semi-axes of the Hotelling’s T-squared ellipses at 95% and 99% confidence levels. Enables users to obtain the coordinates in two or three dimensions at user-defined confidence levels, allowing for the construction of 2D or 3D ellipses with customized confidence levels. Bro and Smilde (2014) <DOI:10.1039/c3ay41907j>. Brereton (2016) <DOI:10.1002/cem.2763>.

Last updated 5 months ago

confidence-ellipsehotelling-ellipsehotelling-s-t-squarehotelling-t2hotellings-t2-distributionmultivariate-distributionoutlierspartial-least-squares-regressionpcaplsprincipal-component-analysis

5.06 score 7 stars 11 scripts 293 downloads

ConfidenceEllipse - Computation of 2D and 3D Elliptical Joint Confidence Regions

Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) <DOI:10.1080/00401706.2019.1677270>.

Last updated 7 months ago

confidence-ellipseconfidence-ellipsoidconfidence-regionmultivariate-distributionoutliers-detectionrobust-statistics

4.70 score 1 stars 603 downloads