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.