ks (version 1.6.13)

drvkde: Kernel density derivative estimation

Description

Compute kernel density derivative estimates and standard errors for multivariate data.

Usage

drvkde(x, drv, bandwidth, gridsize, range.x, binned=FALSE, se=TRUE, w)

Arguments

x

data matrix or matrix of binning counts

drv

vector of derivative indices

bandwidth

vector of bandwidths

gridsize

vector of grid sizes

range.x

list of vector of ranges for x

binned

TRUE if x is binned counts or FALSE if x is data matrix

se

flag for computing the standard error of kernel estimate

w

vector of weights (non-negative and sum is equal to sample size)

Value

• Returns a list with fields x.grid - grid points est - kernel estimate of partial derivative of density function indicated by drv se - estimate of standard error of est (if se=TRUE).

Details

The estimates and standard errors are computed over a grid of binned counts x.grid. If the binned counts are not supplied then they are computed inside this function.

If gridsize and range.x are not supplied, they are computed inside this function.

References

Wand, M.P. and Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall/CRC, London.

Examples

## univariate
x <- rnorm(100)
fhat <- drvkde(x=x, drv=0, bandwidth=0.1)    ## KDE of f
fhat1 <- drvkde(x=x, drv=1, bandwidth=0.1)   ## KDE of df/dx