Skip to contents

K selection

select_K.Rd

K selection

Usage

select_K(
  gen,
  K_selection = "tracy_widom",
  coords = NULL,
  Kvals = 1:10,
  criticalpoint = 2.023,
  low = 0.08,
  max.pc = 0.9,
  perc.pca = 90,
  max.n.clust = 10
)

select_K_tw(gen, criticalpoint = 2.0234)

select_K_elbow(gen, low = 0.08, max.pc = 0.9)

select_K_tess(
  gen,
  coords,
  Kvals = 1:10,
  tess_method = "projected.ls",
  ploidy = 2
)

select_K_fc(gen, perc.pca, max.n.clust)

Arguments

gen

a genotype matrix

K_selection

method for performing K selection (options: "tracy_widom" (default), "quick_elbow", or "tess")

coords

coordinates for "tess"

Kvals

values of K to test for "tess"

criticalpoint

if K_selection = "tracy_widom", a numeric value corresponding to the significance level. If the significance level is 0.05, 0.01, 0.005, or 0.001, the criticalpoint should be set to be 0.9793, 2.0234, 2.4224, or 3.2724, respectively (defaults to 2.0234)

low

if K_selection = "quick_elbow", numeric, between zero and one, the threshold that defines whether a principal component explains 'much' of the variance (defaults to 0.08).

max.pc

if K_selection = "quick_elbow", maximum percentage of the variance to capture before the elbow (cumulative sum to PC 'n'; defaults to 0.90).

perc.pca

if K_selection = "find_clusters", a numeric value between 0 and 100 indicating the minimal percentage of the total variance of the data to be expressed by the retained axes of PCA (defaults to 90).

max.n.clust

if K_selection = "find_clusters", an integer indicating the maximum number of clusters to try. Values of 'k' will be picked up between 1 and max.n.clust (defaults to 10)

tess_method

method to use for "tess"

ploidy

ploidy for "tess"

Value

prints the best K value given the specified K selection procedure

Functions

  • select_K_tw(): select K using Tracy-Widom Test

  • select_K_elbow(): select K using PCA and quick_elbow method

  • select_K_tess(): select K using TESS and bestK method

  • select_K_fc(): select K using find.clusters method

Note

uses the tw function

uses the find.clusters function

See also

Other LFMM functions: lfmm_df(), lfmm_do_everything(), lfmm_manhattanplot(), lfmm_qqplot(), lfmm_run(), lfmm_table(), lfmm_test_tidy(), quick_elbow()

Other LFMM functions: lfmm_df(), lfmm_do_everything(), lfmm_manhattanplot(), lfmm_qqplot(), lfmm_run(), lfmm_table(), lfmm_test_tidy(), quick_elbow()

Other LFMM functions: lfmm_df(), lfmm_do_everything(), lfmm_manhattanplot(), lfmm_qqplot(), lfmm_run(), lfmm_table(), lfmm_test_tidy(), quick_elbow()

Other LFMM functions: lfmm_df(), lfmm_do_everything(), lfmm_manhattanplot(), lfmm_qqplot(), lfmm_run(), lfmm_table(), lfmm_test_tidy(), quick_elbow()

Other LFMM functions: lfmm_df(), lfmm_do_everything(), lfmm_manhattanplot(), lfmm_qqplot(), lfmm_run(), lfmm_table(), lfmm_test_tidy(), quick_elbow()