ks (version 1.8.3)

kdde: Kernel density derivative estimate for multivariate data

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)

Arguments

x

matrix of data values

H

bandwidth matrix

h

scalar bandwidth

deriv.order

derivative order (scalar)

gridsize

vector of number of grid points

gridtype

not yet implemented

xmin

vector of minimum values for grid

xmax

vector of maximum values for grid

supp

effective support for standard normal is [-supp, supp]

eval.points

points at which density 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 added to data i.e. when positive=TRUE KDE is carried out on log(x + adj.positive). Default is the minimum of x.

w

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

Value

• The result is kernel density derivative estimate is an object of class kdde:
• x
  data points - same as input
• eval.points
  points at which the density estimate is evaluated
• estimate
  density derivative estimate at eval.points
• H
  bandwidth matrix
• h
  scalar bandwidth (1-d only)
• w
  weights
• deriv.order
  derivative order (scalar)
• deriv.ind
  each row is a vector of partial derivative indices

Details

For d = 1, 2, 3, 4, and if eval.points is not specified, then the density derivative estimate is computed over a grid defined by gridsize (if binned=FALSE) or by bgridsize (if binned=TRUE).

For d = 1, 2, 3, 4, and if eval.points is specified, then the density derivative estimate is computed exactly at eval.points. For d > 4, the kernel density derivative estimate is computed exactly and eval.points must be specified.

The default xmin is min(x) - Hmax*supp and xmax is max(x) + Hmax*supp where Hmax is the maximum of the diagonal elements of H.The default weights w is a vector of all ones.

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.

See Also

kde

Examples

## univariate example
x <- rnorm.mixt(n=100, mus=1, sigmas=1, props=1)
fhat2 <- kdde(x=x, h=hpi(x), deriv.order=2)      ## d^2 f/dx^2 

## bivariate example
data(unicef)
H.scv <- Hscv(x=unicef)
fhat1 <- kdde(x=unicef, H=H.scv, deriv.order=1)   ## gradient vector