create_kernel_plot: Generate Kernel Plot

In MonotonicityTest: Nonparametric Bootstrap Test for Regression Monotonicity

Generate Kernel Plot

Description

Creates a scatter plot of the input vectors X and Y, and overlays a Nadaraya-Watson kernel regression curve using the specified bandwidth.

Usage

create_kernel_plot(X, Y, bandwidth = bw.nrd(X) * (length(X)^-0.1), nrows = 4)

Arguments

X	Vector of x values.
Y	Vector of y values.
bandwidth	Kernel bandwidth used for the Nadaraya-Watson estimator. Can be a single numeric value or a vector of bandwidths. Default is calculated as bw.nrd(X) * (length(X) ^ -0.1).
nrows	Number of rows in the facet grid if multiple bandwidths are provided. Does not do anything if only a single bandwidth value is provided. Default is 4.

Value

A ggplot object containing the scatter plot(s) with the kernel regression curve(s). If a vector of bandwidths is supplied, the plots are put into a grid using faceting.

References

Nadaraya, E. A. (1964). On estimating regression. Theory of Probability and Its Applications, 9(1), 141–142.

Watson, G. S. (1964). Smooth estimates of regression functions. Sankhyā: The Indian Journal of Statistics, Series A, 359-372.

Examples

# Example 1: Basic plot on quadratic function
seed <- 42
set.seed(seed)
X <- runif(500)
Y <- X ^ 2 + rnorm(500, sd = 0.1)
plot <- create_kernel_plot(X, Y, bandwidth = bw.nrd(X) * (length(X) ^ -0.1))

MonotonicityTest documentation built on June 8, 2025, 10:44 a.m.