fava_norm: Compute the normalized Fst of a matrix of compositional...

In FAVA: Quantify Compositional Variability Across Relative Abundance Vectors

Compute the normalized Fst of a matrix of compositional vectors

Description

This function computes the normalized Fst given the number of rows and the mean abundance of the most abundant category. We employ the normalization employed in the FSTruct package by Morrison, Alcala, and Rosenberg (2020) \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.1111/1755-0998.13647")}.

Usage

fava_norm(relab_matrix, K = ncol(relab_matrix))

Arguments

relab_matrix

A matrix or data frame with rows containing non-negative entries that sum to 1. Each row represents a sample, each column represents a category, and each entry represents the abundance of that category in the sample. If relab_matrix contains any metadata, it must be on the left-hand side of the matrix, the right K entries of each row must sum to 1, and K must be specified. Otherwise, all entries of each row must sum to 1.

K

Optional; an integer specifying the number of categories in the data. Default is K=ncol(relab_matrix).

Value

A numeric value between 0 and 1.

Examples

# Compute the weighted fava of
# the following compositional vectors:
q1 = c(1,   0,   0,   0)
q2 = c(0.5, 0.5, 0,   0)
q3 = c(1/4, 1/4, 1/4, 1/4)
q4 = c(0,   0,   1,   0)
relative_abundances = matrix(c(q1, q2, q3, q4),
                  byrow = TRUE, nrow = 4)

fava_norm(relative_abundances)

FAVA documentation built on April 4, 2025, 4:47 a.m.