GRalgo-calcCriteriaGR: Assessment of clustering quality

In bozenne/MRIaggr: Management, Display, and Processing of Medical Imaging Data

Description

Compute several quality indexes of a two group clustering. For internal use.

Usage

calcCriteriaGR(contrast, groups, W = NULL, sigma = NULL, breaks = NULL, 
         rm.warning = TRUE, criterion.transition = FALSE, criterion.sdfront = FALSE,
         criterion.entropy = TRUE, criterion.Kalinsky = TRUE, criterion.Laboure = TRUE)

Arguments

contrast	the contrast value of each observations. numeric vector. REQUIRED.
groups	the indicator of group membership. logical vector. REQUIRED.
W	the neighbourhood matrix. dgCMatrix or NULL leading to not compute the d1 criterion.
sigma	the sigma_max that have been used in the GR algorithm. positive numeric vector.
breaks	the break points to use to categorize the contrast distribution. numeric vector.
rm.warning	should warning be displayed. logical.
criterion.transition	should the boundary criterion based on the transition levels be computed ? logical.
criterion.sdfront	should the boundary criterion based on the standard deviation be computed ? logical.
criterion.entropy	should the region criterion based on the entropy be computed ? logical.
criterion.Kalinsky	should the region criterion based on the Kalinsky index be computed ? logical.
criterion.Laboure	should the region criterion based on the Laboure index be computed ? logical.

References

Chantal Revol-Muller, Francoise Peyrin, Yannick Carrillon and Christophe Odet. Automated 3D region growing algorithm based on an assessment function. Pattern Recognition Letters, 23:137-150,2002.

bozenne/MRIaggr documentation built on May 13, 2019, 1:39 a.m.