predict_true_entropy_from_counts: predict_true_entropy_from_counts

In Benjamin-Vincent-Lab/binfotron: Binfotron Bioinformatics Analysis Tools Suite

predict_true_entropy_from_counts

Description

Uses an elastic net model to predict true Shannon entropy (ie Shannon entropy at an abundance of 100 million). Two models are used to cover different abundance ranges. 2-1024 is one model 1025-65536 is another. Lastly, if the abundance and Shannon entropy are over a threshhold predetermined to produce results within 95

Usage

predict_true_entropy_from_counts(
  my_counts,
  min_abundance = 2,
  should_screen_counts = TRUE
)

Arguments

my_counts	vector of postive integers
min_abundance	Integer to indicate minimun number of counts needed (ie. sum(my_counts)) to get a valid prediction.
should_screen_counts	Boolean to indicate if the counts should be screened for valid data. Set to false if the data has already been checked by another function.

Value

Modeled prediction of diversity

See Also

Other diversity: chao1(), dXX_index(), evenness(), get_corrected_entropy_rdata_2_4096_path(), get_corrected_entropy_rdata_4097_65536_path(), get_corrected_entropy_rdata_ab1025_65K_ent1_8_path(), get_corrected_entropy_rdata_ab8_1024_ent1_8_path(), has_sufficient_abundance_for_entropy(), inv_simpson(), predict_true_entropy_from_diversity(), screen_counts(), shannon_entropy()

Benjamin-Vincent-Lab/binfotron documentation built on April 11, 2025, 10:05 p.m.