# neighborhood

##### Nearest neighborhoods for kernel smoothing

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.

- Keywords
- smooth

##### 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`

.

##### References

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

##### See Also

##### Examples

```
# NOT RUN {
d <- SimSurv(20)
neighborhood(d$X2)
# }
```

*Documentation reproduced from package prodlim, version 2019.11.13, License: GPL (>= 2)*