R：将[KDE]密度图转换为cdf吗？

Question

Data: 34,46,47,48,52,53,55,56,56,56,57,58,59,59,68

Density Plot

ECDF

What I'd like to do is take the derived density plot and turn it into a cumulative distribution frequency to derive %'s from. And vice versa. My hope is to use the kernel density estimation specifically to derive a smoothed cumulative distribution function. I don't wish to rely on the raw data points to do a ECDF, but use the KDE to do a CDF.

Edit:

I see there is a KernelSmoothing.CDF, might this be the solution? If it is, I have no idea how to implement it so far.

Mathworks has an example of what I'm trying to do, converting from an ECDF to a KECDF under "Compute and plot the estimated cdf evaluated at a specified set of values."

http://www.mathworks.com/help/stats/examples/nonparametric-estimates-of-cumulative-distribution-functions-and-their-inverses.html?requestedDomain=www.mathworks.com

although I think the implementation is fairly sloppy. Considering a polynomial regression line would be a better fit.

Answer 1

library("DiagTest3Grp", lib.loc='~/R/win-library/3.2")

data <- c(34,46,47,48,52,53,55,56,56,56,57,58,59,59,68)
bw <- BW.ref(data)
x0 <- seq(0, 100, .1) 
KS.cdfvec <- Vectorize(KernelSmoothing.cdf, vectorize.args = "c0")
x0.cdf <- KS.cdfvec(xx = data, c0 = x0, bw = bw)
plot(x0, x0.cdf, type = "l")

I still need to figure out how to derive y given x, but this was a major help