Numerical Partial Derivative Estimation – the {NNS} package

[This article was first published on R-posts.com, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

NNS (v0.5.5) now on CRAN has an updated partial derivative routine dy.d_() . This function estimates true average partial derivatives, as well as ceteris paribus conditions for points of interest.

Example below on the syntax for estimating first derivatives of the function y = x_1^2 * x_2^2 , for the points x_1 = 0.5 and x_2 = 0.5, and for both regressors x_1 and x_2.

set.seed(123)
x_1 = runif(1000)
x_2 = runif(1000)
y = x_1 ^ 2 * x_2 ^ 2 dy.d_(cbind(x_1, x_2), y, wrt = 1:2, eval.points = t(c(.5,.5)))["First",] [[1]] [1] 0.2454744 [[2]] [1] 0.2439307

The analytical solution for both regressors at x_1 = x_2 = 0.5 is 0.25.

The referenced paper gives many more examples, comparing dy.d_() to kernel regression gradients and OLS coefficients.

For even more NNS capabilities, check out the examples at GitHub:
https://github.com/OVVO-Financial/NNS/blob/NNS-Beta-Version/examples/index.md


Reference Paper:
Vinod, Hrishikesh D. and Viole, Fred, Comparing Old and New Partial Derivative Estimates from Nonlinear Nonparametric Regressions
https://ssrn.com/abstract=3681104 

Supplemental Materials:
https://ssrn.com/abstract=3681436



Numerical Partial Derivative Estimation – the {NNS} package was first posted on September 3, 2020 at 5:20 pm.
©2020 “R-posts.com“. Use of this feed is for personal non-commercial use only. If you are not reading this article in your feed reader, then the site is guilty of copyright infringement. Please contact me at tal.galili@gmail.com

To leave a comment for the author, please follow the link and comment on their blog: R-posts.com.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)