window_fava: Compute FAVA in sliding windows.

In FAVA: Quantify Compositional Variability Across Relative Abundance Vectors

Compute FAVA in sliding windows.

Description

This function computes FAVA in sliding window slices of a dataset.

Usage

window_fava(
  relab_matrix,
  window_size,
  window_step = 1,
  group = NULL,
  index = NULL,
  time = NULL,
  w = NULL,
  S = NULL,
  K = NULL,
  normalized = FALSE,
  alpha = 0.5
)

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.
window_size	An integer number specifying the number of samples per window.
window_step	Optional; an integer specifying the distance between the first entry of adjacent windows. Default is window_step=1.
group	Optional; a string specifying the name of the column that describes which group each row (sample) belongs to. Use if relab_matrix is a single matrix containing multiple groups of samples you wish to compare.
index	Optional; a string specifying the name of the column in relab_matrix containing an index for each sample. For example, if relab_matrix contains time series data, index would be the column containing the time of each sample. If index is not specified but time is, time is by default used as the index.
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.
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)).
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, equaling 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)).
K	Optional; an integer specifying the number of categories in the data. Default is K=ncol(relab_matrix).
normalized	Optional; should normalized FAVA be used? Default is normalized = FALSE; use normalized = TRUE to compute normalized FAVA. FAVA can only be normalized if it is not weighted.
alpha	Optional; number between 0 and 1 specifying the opacity of the horizontal lines plotted. Default is alpha = 0.5.

Value

A list of values of FAVA for each window.

Examples

A = matrix(c(.3,.7,0,.1,0,.9,.2,.5,.3,.1,.8,.1,.3,.4,.3,.6,.4,0,0,.5,.5),
           ncol = 3, byrow = TRUE)
window_out = window_fava(relab_matrix = A, window_size = 4, normalized = TRUE)

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