wkde2d: Calculate a bivariate weighted kernel density estimate

In DeconWK: Deconvolution by Weighted Kernels

Description

Calculate a bivariate weighted kernel density estimate.

Usage

wkde.2d(y, Eval, w, H)

Arguments

y	the observed values; matrix with two columns.
Eval	two dimensional grid (matrix with two columns) on which the deconvolution density estimate is to be calculated.
w	the weights to be used.
H	the matrix of smoothing parameter to be used.

Details

If "w" is not specified, it defaults to a vector of ones.

If "H" is not a matrix, it defaults to Hpi(y).

Value

A vector containing the bivariate deconvolution density estimate.

Author(s)

Martin L Hazelton [e-mail]

References

Hazelton, M.L. and Turlach, B.A. (2009). Nonparametric density deconvolution by weighted kernel estimators, Statistics and Computing 19(3): 217–228. http://dx.doi.org/10.1007/s11222-008-9086-7.

See Also

w.hat.mv, wkde.contour, Hpi

Examples

  library(ks)
  Age <- framingham[,2]
  Age.lim.2 <- 56
  SBP1.A <- framingham[Age>=Age.lim.2,3] # SBP, measure 1, Exam 2 
  SBP2.A <- framingham[Age>=Age.lim.2,4] # SBP, measure 2, Exam 2 
  SBP1.B <- framingham[Age>=Age.lim.2,5] # SBP, measure 1, Exam 3 
  SBP2.B <- framingham[Age>=Age.lim.2,6] # SBP, measure 2, Exam 3 
  sigma.fram.A <- sd(SBP1.A-SBP2.A)
  sigma.fram.B <- sd(SBP1.B-SBP2.B)
  Sigma.fram <- diag(c(sigma.fram.A,sigma.fram.B))^2
  SBP.A <- SBP1.A
  SBP.B <- SBP1.B
  SBP.bi <- cbind(SBP.A,SBP.B)
  H.fram <- Hpi(SBP.bi)

  y1.grid <- seq(min(SBP.bi[, 1]) - 0.5 * sd(SBP.bi[, 1]),
                 max(SBP.bi[, 1]) + 0.5 * sd(SBP.bi[, 1]), length = 25)
  y2.grid <- seq(min(SBP.bi[, 2]) - 0.5 * sd(SBP.bi[, 2]),
                 max(SBP.bi[, 2]) + 0.5 * sd(SBP.bi[, 2]), length = 25)
  Eval <- as.matrix(expand.grid(y1.grid, y2.grid))

  w <-  w.hat.mv(SBP.bi, Sigma.fram, H.fram, gamma = 0.4)
  fhat <- wkde.2d(SBP.bi, Eval = Eval, w = w, H = H.fram)
  str(fhat)

DeconWK documentation built on May 2, 2019, 6:08 p.m.