Given an empirical probability density function, how to find the value (in R) at which the density peaks? [duplicate]

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This question already has answers here:

Finding local maxima and minima (17 answers)

Closed 8 years ago.

I calculate the density function for the following data:

> dput(mydat)  
c(-20, -13, 30, 4, -4, 34, 27, 19, 13.5, 15, 13, 18, 10, 12, 
21, -0.769999999999996, 2.5, -7, 0, -30.6, 6.39999999999999, 
-18.6, -0.199999999999989, -20.4, -19.9, 4.60000000000001, -19.4, 
4.5, -9, -15, 9, -1, -14, 8, 6, -17, 5, 7)  

> myden = density(mydat)  # default kernel and bandwidth  

which gives me this result:

I want to find the location of the two density peaks. I initially thought of using diff() on myden$y and then check for all locations where there is a sign change, using this as a condition to select the X-axis values. I tried it on a few test vectors but I was not getting the expected result and I suspect it is not that simplistic.

Is there a straightforward way to accomplish this? I want a solution that will be repeatable because I will do this as part of a random simulation study with ~ e+05 realisations, and it could happen that the number of peaks vary across the simulation.

Answer 1

I often use pastecs::turnpoints to find local maxima and minima.

Answer 2

Use which.max:

myden$x[which.max(myden$y)]
# [1] 5.91428

You can test this visually:

plot(myden, col='red')
abline(v=myden$x[which.max(myden$y)])