normalize_weights: Normalize Edge Weights

Description

Rescales the weight matrix. Row normalization is what turns a transition count matrix into the transition probabilities that TNA models use.

Usage

normalize_weights(
  x,
  method = c("row", "column", "max", "sum", "minmax"),
  keep_format = FALSE,
  directed = NULL
)

Value

A cograph_network with rescaled weights, or the input format when keep_format = TRUE.

Arguments

x

Network input.

method

How to rescale:

"row": (default) each row sums to 1

"column"

each column sums to 1

"max"

divide by the largest absolute weight

"sum"

divide by the total of all weights

"minmax"

rescale the non-zero weights to [0, 1]

keep_format

Logical. Return the input format when TRUE.

directed

Logical or NULL. If NULL (default), auto-detect.

Details

A row (or column, or the whole matrix) whose total is zero is left at zero rather than producing NaN: there is nothing to distribute. Rows with a zero total are reported in a cograph_zero_norm warning so that the zeros are a stated result rather than a silent one.

"minmax" maps the weakest edge to .Machine$double.eps rather than to exactly 0, because 0 is how this representation stores "no edge": mapping to it would delete the weakest edge instead of rescaling it.

"max", "sum" and "minmax" rescale each edge independently and therefore keep any extra edge columns. "row" and "column" scale an edge by a total that differs at its two endpoints, so they break symmetry and return a directed network.

Row and column normalization are meaningful on directed networks. On an undirected network they still work but break symmetry, so the result is returned as directed.

Examples

Run this code

counts <- matrix(c(0, 3, 1,
                   2, 0, 4,
                   5, 1, 0), 3, 3, byrow = TRUE)
rownames(counts) <- colnames(counts) <- c("A", "B", "C")

normalize_weights(counts, method = "row")
normalize_weights(counts, method = "max")