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Last data update: 2014.03.03

R: Bivariate kernel density estimation for rounded data

dbivr

R Documentation

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 = 1000)

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

# 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")
## End(Not run)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Kernelheaping)
Loading required package: MASS
Loading required package: ks
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
Loading required package: misc3d
Loading required package: mvtnorm
Loading required package: rgl
Loading required package: sparr
Loading required package: spatstat
Loading required package: nlme
Loading required package: rpart

spatstat 1.45-2       (nickname: 'Caretaker Mode') 
For an introduction to spatstat, type 'beginner' 


Attaching package: 'spatstat'

The following object is masked from 'package:MASS':

    area



Welcome to 'sparr': SPAtial Relative Risk (v0.3-8)
T.M. Davies, M.L. Hazelton & J.C. Marshall
-type 'help("sparr")' for details
-type 'citation("sparr")' for how to cite use of this package

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Kernelheaping/dbivr.Rd_%03d_medium.png", width=480, height=480)
> ### Name: dbivr
> ### Title: Bivariate kernel density estimation for rounded data
> ### Aliases: dbivr
> 
> ### ** Examples
> 
> # 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: 
> ##D xtrue=rmvnorm.mixt(n=1000, mus=mus, Sigmas=Sigmas, props=props)
> ##D roundvalue=2
> ##D xrounded=plyr::round_any(xtrue,roundvalue)
> ##D est <- dbivr(xrounded,roundvalue=roundvalue,burnin=5,samples=10)
> ##D 
> ##D #Plot corrected and Naive distribution
> ##D plot(est,trueX=xtrue)
> ##D #for comparison: plot true density
> ##D  dens=dmvnorm.mixt(x=expand.grid(est$Mestimates$eval.points[[1]],est$Mestimates$eval.points[[2]]),
> ##D   mus=mus, Sigmas=Sigmas, props=props)
> ##D  dens=matrix(dens,nrow=length(est$gridx),ncol=length(est$gridy))
> ##D  contour(dens,x=est$Mestimates$eval.points[[1]],y=est$Mestimates$eval.points[[2]],
> ##D     xlim=c(min(est$gridx),max(est$gridx)),ylim=c(min(est$gridy),max(est$gridy)),main="True Density")
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>