wkde2d: Weighted 2D kernel density estimation

In powellgenomicslab/Nebulosa: Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation

Weighted 2D kernel density estimation

Description

Weighted 2D kernel density estimation

Usage

wkde2d(x, y, w, h, adjust = 1, n = 100, lims = c(range(x), range(y)))

Arguments

x	Dimension 1
y	Dimension 2
w	Weight variable
h	vector of bandwidths for x and y directions. Defaults to normal reference bandwidth (ks::hpi). A scalar value will be taken to apply to both directions.
adjust	Bandwidth adjustment
n	Number of grid points in each direction. Can be scalar or a length-2 integer vector.
lims	The limits of the rectangle covered by the grid as c(xl, xu, yl, yu).

Value

A list of three components.

• x, y The x and y coordinates of the grid points, vectors of length n.

• z An n[1] by n[2] matrix of the weighted estimated density: rows correspond to the value of x, columns to the value of y.

Author(s)

Jose Alquicira-Hernandez

Examples

set.seed(1)
x <- rnorm(100)

set.seed(2)
y <- rnorm(100)

set.seed(3)
w <- sample(c(0, 1), 100, replace = TRUE)

dens <- Nebulosa:::wkde2d(x, y, w)

powellgenomicslab/Nebulosa documentation built on March 3, 2024, 8:23 p.m.