kdde: Kernel density derivative estimate

Description

Kernel density derivative estimate for 1- to 6-dimensional data.

Usage

kdde(x, H, h, deriv.order=0, gridsize, gridtype, xmin, xmax, supp=3.7,  eval.points, binned=FALSE, bgridsize, positive=FALSE, adj.positive, w, deriv.vec=TRUE, verbose=FALSE)
"predict"(object, ..., x)

Arguments

matrix of data values

H,h

bandwidth matrix/scalar bandwidth. If these are missing, Hpi or hpi is called by default.

deriv.order

derivative order (scalar)

gridsize

vector of number of grid points

gridtype

not yet implemented

xmin,xmax

vector of minimum/maximum values for grid

supp

effective support for standard normal

eval.points

points at which estimate is evaluated

binned

flag for binned estimation. Default is FALSE.

bgridsize

vector of binning grid sizes

positive

flag if 1-d data are positive. Default is FALSE.

adj.positive

adjustment applied to positive 1-d data

vector of weights. Default is a vector of all ones.

deriv.vec

flag to compute all derivatives in vectorised derivative. Default is TRUE. If FALSE then only the unique derivatives are computed.

verbose

flag to print out progress information. Default is FALSE.

object

object of class kdde

...

other parameters

Value

x: data points - same as input
eval.points: points at which the estimate is evaluated
estimate: density derivative estimate at eval.points
h: scalar bandwidth (1-d only)
H: bandwidth matrix
gridtype: "linear"
gridded: flag for estimation on a grid
binned: flag for binned estimation
names: variable names
w: weights
deriv.order: derivative order (scalar)
deriv.ind: each row is a vector of partial derivative indices

Details

For each partial derivative, for grid estimation, the estimate is a list whose elements correspond to the partial derivative indices in the rows of deriv.ind. For points estimation, the estimate is a matrix whose columns correspond to rows of deriv.ind.

If the bandwidth H is missing from kdde, then the default bandwidth is the plug-in selector Hpi. Likewise for missing h. The effective support, binning, grid size, grid range, positive parameters are the same as kde.

Examples

Run this code

set.seed(8192)
x <- rmvnorm.mixt(1000, mus=c(0,0), Sigmas=invvech(c(1,0.8,1)))
fhat <- kdde(x=x, binned=TRUE, deriv.order=1) ## gradient [df/dx, df/dy]
predict(fhat, x=x[1:5,])

## See other examples in ? plot.kdde