convol {gplm}

Kernel convolution

Description

Calculates the convolution of data with a kernel function.

Usage

convol(x, h = 1, grid = NULL, y = 1, w = 1, p = 2, q = 2,
       product = TRUE, sort = TRUE)

Arguments

x

n x d matrix, data

h

scalar or 1 x d, bandwidth(s)

grid

m x d matrix, where to calculate the convolution (default = x)

y

n x c matrix, optional responses

w

scalar or n x 1 or 1 x m or n x m, optional weights

p

integer or text, see kernel.function

q

integer, see kernel.function

product

(if d>1) product or spherical kernel

sort

logical, TRUE if data need to be sorted

Details

The kernel convolution which is calculated is \sum_i K_h(x_i - grid_{j})\,y_i\,w_{ij} for i=1,...,n and j=1,...,m. The kernel function is determined by the kernel parameters p and q, see kernel.function. The default kernel is the biweight (quartic) kernel function. Note that the DLL requires the data matrix to be sorted by its first column.

Value

m x c matrix

Author(s)

Marlene Mueller

See Also

kernel.function, kde, kreg

Examples

  n <- 100
  x <- rnorm(n)
  convol(x,h=0.8,grid=-3:3)/n  ## estimates density of x at points -3:3