Kernelheaping (version 2.3.0)

dbivr: Bivariate kernel density estimation for rounded data

Description

Bivariate kernel density estimation for rounded data

Usage

dbivr(
  xrounded,
  roundvalue,
  burnin = 2,
  samples = 5,
  adaptive = FALSE,
  gridsize = 200
)

Arguments

xrounded

rounded values from which to estimate bivariate density, matrix with 2 columns (x,y)

roundvalue

rounding value (side length of square in that the true value lies around the rounded one)

burnin

burn-in sample size

samples

sampling iteration size

adaptive

set to TRUE for adaptive bandwidth

gridsize

number of evaluation grid points

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 (x)

gridy

Vector Grid on which density is evaluated (y)

resultDensity

Array with Estimated Density for each iteration

resultX

Matrix of true latent values X estimates

delaigle

Matrix of Delaigle estimator estimates

Examples

# NOT RUN {
# Create Mu and Sigma  -----------------------------------------------------------
mu1 <- c(0, 0)
mu2 <- c(5, 3)
mu3 <- c(-4, 1)
Sigma1 <- matrix(c(4, 3, 3, 4), 2, 2)
Sigma2 <- matrix(c(3, 0.5, 0.5, 1), 2, 2)
Sigma3 <- matrix(c(5, 4, 4, 6), 2, 2)
# Mixed Normal Distribution -------------------------------------------------------
mus <- rbind(mu1, mu2, mu3)
Sigmas <- rbind(Sigma1, Sigma2, Sigma3)
props <- c(1/3, 1/3, 1/3)
# }
# NOT RUN {
xtrue=rmvnorm.mixt(n=1000, mus=mus, Sigmas=Sigmas, props=props)
roundvalue=2
xrounded=plyr::round_any(xtrue,roundvalue)
est <- dbivr(xrounded,roundvalue=roundvalue,burnin=5,samples=10)

#Plot corrected and Naive distribution
plot(est,trueX=xtrue)
#for comparison: plot true density
 dens=dmvnorm.mixt(x=expand.grid(est$Mestimates$eval.points[[1]],est$Mestimates$eval.points[[2]]),
  mus=mus, Sigmas=Sigmas, props=props)
 dens=matrix(dens,nrow=length(est$gridx),ncol=length(est$gridy))
 contour(dens,x=est$Mestimates$eval.points[[1]],y=est$Mestimates$eval.points[[2]],
    xlim=c(min(est$gridx),max(est$gridx)),ylim=c(min(est$gridy),max(est$gridy)),main="True Density")
# }