如何使用“ks"和“rgl"从 R 内置的 3D 核密度图中提取值 [英] How to extract values from a 3D kernel density plot built in R using 'ks' and 'rgl'

问题描述

我一直在使用 'ks' 包和 'rgl' 包来生成 3D 内核密度估计和这些的 3D 图.第一部分效果很好(下面是简要示例).我无法弄清楚的是，是否有可能首先提取用于构建内核的给定 xyz 位置的内核值.换句话说，提取 3D 图中点的值，类似于用于 'raster' 包中 2D 表面的提取命令.有没有人有做这样的事情的经验可以为我指明正确的方向?非常感谢.-DJ

I've been using the 'ks' package along with the 'rgl' package to produce 3D kernel density estimates and 3D plots of these. This first part has worked out fine (brief example below). What I can't figure out is if it's possible to extract the values of the kernels for the given xyz locations used to build the kernels in the first place. In other words, extract the values for points in a 3D plot, akin to the extract command used for 2D surfaces in the 'raster' package. Does anyone have experience doing something like this that can point me in the right direction? Thanks much. -DJ

library("rgl")
library("ks")

# call the plug-in bandwidth estimator
H.pi <- Hpi(b,binned=TRUE) ## b is a matrix of x,y,z points

# calculate the kernel densities
fhat2 <- kde(b, H=H.pi)

#plot the 50% and 95% kernels in gray and blue
plot(fhat2,cont=c(50,95),colors=c("gray","blue"),drawpoints=TRUE
    ,xlab="", ylab="", zlab="",size=2, ptcol="white", add=FALSE, box=TRUE, axes=TRUE) 




#Structure of fhat2. Original df consists of ~6000 points.  

List of 9
 $ x          : num [1:6173, 1:3] -497654 -497654 -497929 -498205 -498205 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:6173] "50" "57" "70" "73" ...
  .. ..$ : chr [1:3] "x" "max_dep" "y"

$ eval.points:List of 3
  ..$ : num [1:51] -550880 -546806 -542733 -538659 -534586 ...
  ..$ : num [1:51] -7.9 -4.91 -1.93 1.06 4.05 ...
  ..$ : num [1:51] -376920 -374221 -371522 -368823 -366124 ...

$ estimate   : num [1:51, 1:51, 1:51] 0 0 0 0 0 ...

$ H          : num [1:3, 1:3] 3.93e+07 -2.97e+03 8.95e+06 -2.97e+03 2.63e+01 ...

$ gridtype   : chr [1:3] "linear" "linear" "linear"

$ gridded    : logi TRUE

$ binned     : logi FALSE

$ names      : chr [1:3] "x" "max_dep" "y"

$ w          : num [1:6173] 1 1 1 1 1 1 1 1 1 1 ...
 - attr(*, "class")= chr "kde"

推荐答案

试试这个

## from ?plot.kde
library(ks)
library(MASS)
 data(iris)

 ## trivariate example
 fhat <- kde(x=iris[,1:3])

## this indicates the orientation
image(fhat$eval.points[[1]], fhat$eval.points[[2]], apply(fhat$estimate, 1:2, sum))
points(fhat$x[,1:2])

library(raster)

## convert to RasterBrick from raw array
## with correct orientation relative to that of ?base::image
b <- brick(fhat$estimate[,ncol(fhat$estimate):1,], 
    xmn = min(fhat$eval.points[[1]]), xmx = max(fhat$eval.points[[1]]), ymn = min(fhat$eval.points[[2]]), ymx = max(fhat$eval.points[[2]]), 
    transpose = TRUE)

## check orientation
plot(calc(b, sum))
points(fhat$x[,1:2])

现在我们很高兴，因为光栅功率很好.

Now we are happy because raster power is good.

plot(b)

## note this is a matrix with nrows = nrow(fhat$x), ncols = nlayers(b)
extract(b, fhat$x[,1:2])

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