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MRIaggr (version 1.1.5)

calcCriteriaGR: Assessment of clustering quality

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.