dclass {Kernelheaping}

Kernel density estimation for classified data

Description

Kernel density estimation for classified data

Usage

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

Arguments

xclass

classified values; matrix with two columns: lower and upper value

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

dFunc

character optional density (with "d", "p" and "q" functions) function name for parametric estimation such as "norm" "gamma" or "lnorm"

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)
xclass <- cbind(classes[as.numeric(xclass)], classes[as.numeric(xclass) + 1])
densityEst <- dclass(xclass=xclass, burnin=20, samples=50, evalpoints=1000)
plot(densityEst$Mestimates~densityEst$gridx ,lwd=2, type = "l")