nnbr: nearest neighbour distances from data in one or two...

In sm: Smoothing Methods for Nonparametric Regression and Density Estimation

nearest neighbour distances from data in one or two dimensions

Description

This function calculates the k nearest neighbour distance from each value in x to the remainder of the data. In two dimensions, Euclidean distance is used after standardising the data to have unit variance in each component.

Usage

nnbr(x, k)

Arguments

x	the vector, or two-column matrix, of data.
k	the required order of nearest neighbour.

Details

see Section 1.7.1 of the reference below.

Value

the vector of nearest neighbour distances.

References

Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.

See Also

none.

Examples

x  <- rnorm(50)
hw <- nnbr(x, 10)
hw <- hw/exp(mean(log(hw)))
sm.density(x, h.weights=hw)

sm documentation built on May 29, 2024, 2:28 a.m.