Split observations by k-means clustering groups.

Aligns and groups observations based on k-means clustering, enabling observation splits by cluster groups.

Usage

align_kmeans(..., data = NULL, active = NULL)

Arguments

...

Arguments passed on to stats::kmeans

iter.max

the maximum number of iterations allowed.

nstart

if centers is a number, how many random sets should be chosen?

algorithm

character: may be abbreviated. Note that "Lloyd" and "Forgy" are alternative names for one algorithm.

trace

logical or integer number, currently only used in the default method ("Hartigan-Wong"): if positive (or true), tracing information on the progress of the algorithm is produced. Higher values may produce more tracing information.

data

A numeric matrix to be used by k-means. By default, it will inherit from the layout matrix.

active

A active() object that defines the context settings when added to a layout.

Discrete Axis Alignment

It is important to note that we consider rows as observations, meaning vec_size(data)/NROW(data) must match the number of observations along the axis used for alignment (x-axis for a vertical stack layout, y-axis for a horizontal stack layout).

Examples

ggheatmap(matrix(rnorm(81), nrow = 9)) +
    anno_top() +
    align_kmeans(3L)
#> → heatmap built with `geom_tile()`