gini_simpson_mean: Compute the mean Gini-Simpson index of the rows in a matrix...

In FAVA: Quantify Compositional Variability Across Relative Abundance Vectors

Compute the mean Gini-Simpson index of the rows in a matrix of compositional vectors

Description

This function computes the mean Gini-Simpson index, a statistical measure of variability known in population genetics as heterozygosity, of a set of vectors of non-negative entries which sum to 1. The function returns a number between 0 and 1 which quantifies the mean variability of the vectors. Values of 0 are achieved when each vector is a permutation of (1,0,..., 0). The value approaches 1 as the number of categories K increases when the vectors are equal to (1/K, 1/K, ..., 1/K).

Usage

gini_simpson_mean(
  relab_matrix,
  K = NULL,
  S = NULL,
  w = NULL,
  time = NULL,
  group = NULL
)

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).
S	Optional; a K x K similarity matrix with diagonal elements equal to 1 and off-diagonal elements between 0 and 1. Entry S[i,k] for i!=k is the similarity between category and i and category k, equalling 1 if the categories are to be treated as identical and equaling 0 if they are to be treated as totally dissimilar. The default value is S = diag(ncol(relab_matrix)).
w	Optional; a vector of length I with non-negative entries that sum to 1. Entry w[i] represents the weight placed on row i in the computation of the mean abundance of each category across rows. The default value is w = rep(1/nrow(relab_matrix), nrow(relab_matrix)).
time	Optional; a string specifying the name of the column that describes the sampling time for each row. Include if you wish to weight FAVA by the distance between samples.
group	Optional; a string (or vector of strings) specifying the name(s) of the column(s) that describes which group(s) each row (sample) belongs to. Use if relab_matrix is a single matrix containing multiple groups of samples you wish to compare.

Value

A numeric value between 0 and 1.

Examples

# To compute the mean Gini-Simpson index 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)

# we could compute the mean manually:
mean(sapply(list(q1, q2, q3, q4), gini_simpson))

# Or we could use gini_simpson_mean:
relative_abundances = matrix(c(q1, q2, q3, q4),
                  byrow = TRUE, nrow = 4)

gini_simpson_mean(relative_abundances)

# Incoporating weights:

# Compute mean Gini-Simpson index ignoring
# rows 2 and 3
row_weights = c(0.5, 0, 0, 0.5)
gini_simpson_mean(relative_abundances, w = row_weights)

# Compute mean Gini-Simpson index assuming that
# categories 1 and 2 are identical:
similarity_matrix = diag(4)
similarity_matrix[1,2] = 1
similarity_matrix[2,1] = 1
gini_simpson_mean(relative_abundances, S = similarity_matrix)

# Assume categories 1 and 2 are identical AND
# ignore rows 2 and 4:
row_weights = c(0.5, 0, 0.5, 0)
gini_simpson_mean(relative_abundances, w = row_weights, S = similarity_matrix)

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