feature (version 1.1-3)

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)

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

vector of ranges for x

binned

TRUE if x is binned counts or FALSE (default) if x is data matrix

se

TRUE (default) computes standard error (SE) for kernel estimate or FALSE skips computing SE

Value

• If se=TRUE, it 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.

  Otherwise if se=FALSE, only x.grid and est are returned.

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.

This function doesn't need to be used directly as it called from featureSignif.

References

Wand, M.P. and Jones, M.C. (1995) Kernel Smoothing Chapman and Hall.

See Also

featureSignif