Binarize a netify object at a threshold

thin wrapper around mutate_weights() for the very common case of "dichotomize the weighted network at a cut-point." returns a new netify with edge values coerced to 0/1 based on the supplied threshold (or threshold function).

Usage

binarize(netlet, threshold = 0, strict = FALSE, abs = FALSE, new_name = NULL)

Arguments

netlet: a weighted netify object.
threshold: numeric scalar (default 0 – any nonzero edge becomes 1) or a function f(x) that takes the vector of edge weights and returns a single numeric threshold (e.g., function(x) median(x, na.rm = TRUE) or function(x) quantile(x, 0.75, na.rm = TRUE)). for longitudinal and multilayer networks, the function is evaluated once on the weights pooled across all periods and layers, and the resulting threshold is applied to every layer.
strict: logical. if TRUE, edges with weight strictly greater than the threshold become 1; if FALSE (default), the threshold itself is included (>=). for threshold = 0, the default gives the "any nonzero edge counts" semantics that matches the rest of the package (signed-weight density, homophily-default-threshold, etc.).
abs: logical. if TRUE, compare |x| to the threshold so that negative-magnitude ties also count toward the binarization. defaults to FALSE. when the network contains both positive and negative weights and abs = FALSE, binarize() informs once that negative ties will be dropped.
new_name: optional character. new name for the binarized weight column (default keeps the original name). for multilayer networks, supply one name per layer.

Value

a binarized netify object with is_binary = TRUE.

Details

na cells (e.g., the diagonal under diag_to_NA = TRUE) propagate as na in the output rather than becoming 0. use na.rm = TRUE when summing edges if you want them treated as 0. structural zeros stay zero in every branch – a negative threshold will not promote empty cells to 1, regardless of strict or abs.

Author

cassy dorff, shahryar minhas

Examples

# \donttest{
data(icews)
net <- netify(icews[icews$year == 2010, ],
actor1 = "i", actor2 = "j", symmetric = FALSE, weight = "verbCoop")
# any-nonzero-edge dichotomization
bin0 <- binarize(net)
#> ℹ network has been binarized through transformation
# 75th-percentile of nonzero weights
bin75 <- binarize(net, threshold = function(x) {
nz <- x[x > 0]
quantile(nz, 0.75, na.rm = TRUE)
})
#> ℹ network has been binarized through transformation
# }