从 R 中的核密度估计中获取值 [英] Getting values from kernel density estimation in R
问题描述
我正在尝试获取 R 中股票价格对数的密度估计值.我知道我可以使用 plot(density(x))
绘制它.但是,我实际上想要函数的值.
I am trying to get density estimates for the log of stock prices in R. I know I can plot it using plot(density(x))
. However, I actually want values for the function.
我正在尝试实现内核密度估计公式.这是我到目前为止所拥有的:
I'm trying to implement the kernel density estimation formula. Here's what I have so far:
a <- read.csv("boi_new.csv", header=FALSE)
S = a[,3] # takes column of increments in stock prices
dS=S[!is.na(S)] # omits first empty field
N = length(dS) # Sample size
rseed = 0 # Random seed
x = rep(c(1:5),N/5) # Inputted data
set.seed(rseed) # Sets random seed for reproducibility
QL <- function(dS){
h = density(dS)$bandwidth
r = log(dS^2)
f = 0*x
for(i in 1:N){
f[i] = 1/(N*h) * sum(dnorm((x-r[i])/h))
}
return(f)
}
QL(dS)
任何帮助将不胜感激.已经在这呆了好几天了!
Any help would be much appreciated. Been at this for days!
推荐答案
您可以直接从 密度
函数中提取值:
You can pull the values directly from the density
function:
x = rnorm(100)
d = density(x, from=-5, to = 5, n = 1000)
d$x
d$y
或者,如果您真的想编写自己的核密度函数,这里有一些代码可以帮助您入门:
Alternatively, if you really want to write your own kernel density function, here's some code to get you started:
设置点
z
和x
范围:
z = c(-2, -1, 2)
x = seq(-5, 5, 0.01)
现在我们将点添加到图形中
Now we'll add the points to a graph
plot(0, 0, xlim=c(-5, 5), ylim=c(-0.02, 0.8),
pch=NA, ylab="", xlab="z")
for(i in 1:length(z)) {
points(z[i], 0, pch="X", col=2)
}
abline(h=0)
在每个点周围放置法线密度:
Put Normal density's around each point:
## Now we combine the kernels,
x_total = numeric(length(x))
for(i in 1:length(x_total)) {
for(j in 1:length(z)) {
x_total[i] = x_total[i] +
dnorm(x[i], z[j], sd=1)
}
}
并将曲线添加到图中:
lines(x, x_total, col=4, lty=2)
最后,计算完整的估计:
Finally, calculate the complete estimate:
## Just as a histogram is the sum of the boxes,
## the kernel density estimate is just the sum of the bumps.
## All that's left to do, is ensure that the estimate has the
## correct area, i.e. in this case we divide by $n=3$:
plot(x, x_total/3,
xlim=c(-5, 5), ylim=c(-0.02, 0.8),
ylab="", xlab="z", type="l")
abline(h=0)
这对应于
density(z, adjust=1, bw=1)
上图给出:
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