Kernelheaping (version 1.5)

dclass: Kernel density estimation for classified data

Description

Kernel density estimation for classified data

Usage

dclass(xclass, classes, burnin = 2, samples = 5, boundary = FALSE,
  bw = "nrd0", evalpoints = 200, adjust = 1)

Arguments

xclass

classified values; factor with ordered factor values

classes

numeric vector of classes; Inf as last value is allowed

burnin

burn-in sample size

samples

sampling iteration size

boundary

TRUE for positive only data (no positive density for negative values)

bw

bandwidth selector method, defaults to "nrd0" see density for more options

evalpoints

number of evaluation grid points

adjust

as in density, the user can multiply the bandwidth by a certain factor such that bw=adjust*bw

Value

• The function returns a list object with the following objects (besides all input objects):
• Mestimates
  kde object containing the corrected density estimate
• gridx
  Vector Grid on which density is evaluated
• resultDensity
  Matrix with Estimated Density for each iteration
• resultX
  Matrix of true latent values X estimates

Examples

x=rlnorm(500, meanlog = 8, sdlog = 1)
classes <- c(0,500,1000,1500,2000,2500,3000,4000,5000,6000,8000,10000,15000,Inf)
xclass <- cut(x,breaks=classes)
densityEst <- dclass(xclass=xclass, classes=classes, burnin=2, samples=5, evalpoints=1000)
hist(densityEst$xclass,breaks=densityEst$classes)
lines(densityEst$Mestimates~densityEst$gridx,col="purple",lwd=2)