R: Nearest neighborhoods for kernel smoothing

neighborhood {prodlim} | R Documentation

## Nearest neighborhoods for kernel smoothing

### Description

Nearest neighborhoods for the values of a continuous predictor. The result is used for the conditional Kaplan-Meier estimator and other conditional product limit estimators.

### Usage

    neighborhood(x, bandwidth = NULL, kernel = "box")

### Arguments

`x` | Numeric vector – typically the observations of a continuous random variate.
`bandwidth` | Controls the distance between neighbors in a neighborhood. It can be a decimal, i.e.\ the bandwidth, or the string ‘"smooth"’, in which case `N^{-1/4}` is used, `N` being the sample size, or `NULL` in which case the `dpik` function of the package KernSmooth is used to find the optimal bandwidth.
`kernel` | Only the rectangular kernel ("box") is implemented.

### Value

An object of class 'neighborhood'. The value is a list that includes the unique values of ‘x’ (`values`) for which a neighborhood, consisting of the nearest neighbors, is defined by the first neighbor (`first.nbh`) of the usually very long vector `neighbors` and the size of the neighborhood (`size.nbh`).

Further values are the arguments `bandwidth`, `kernel`, the total sample size `n` and the number of unique values `nu`.

### Author(s)

Thomas Gerds

### References

Stute, W. "Asymptotic Normality of Nearest Neighbor Regression Function Estimates", The Annals of Statistics, 1984,12,917–926.

### See Also

`dpik`, `prodlim`

### Examples

    d <- SimSurv(20)
    neighborhood(d$X2)

[Package prodlim version 2018.04.18 Index]