dbivr: Bivariate kernel density estimation for rounded data In Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

Description

Bivariate kernel density estimation for rounded data

Usage

 ```1 2 3 4 5 6 7 8``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```# 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) ```

Kernelheaping documentation built on May 11, 2021, 5:08 p.m.