PAM (k-medoids) cost

Sum, over the target rows, of the Euclidean distance to the nearest training row: the cost that partitioning around medoids minimizes when the training rows are the medoids. Smaller is better.

Usage

pam_score(X, X0)

Arguments

X

A numeric matrix of the training set (row: individual; column: marker or PC).

X0

A numeric matrix of the target rows (the test set), with the same columns as X.

Value

A number.

Details

See criteria for the list of criteria and the package website for one page per criterion.

References

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: an introduction to cluster analysis. Wiley. <doi:10.1002/9780470316801> Guo, T., et al. (2019). Optimal designs for genomic selection in hybrid crops. Molecular Plant, 12, 390-401. <doi:10.1016/j.molp.2018.12.022> Rio, S., et al. (2021). Genomic prediction and training set optimization in a structured Mediterranean oat population. Theoretical and Applied Genetics, 134, 3595-3609. <doi:10.1007/s00122-021-03916-w>

Author

Jen-Hsiang Ou

Examples

data(geno)
pam_score(geno[1:50, ], geno[51:100, ])
#> [1] 6588.776