https://github.com/JuliaStats/Loess.jl

Local regression, so smooooth!
https://github.com/JuliaStats/Loess.jl

Last synced: 3 months ago
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Local regression, so smooooth!

• Host: GitHub
• URL: https://github.com/JuliaStats/Loess.jl
• Owner: JuliaStats
• License: other
• Created: 2013-09-14T00:00:30.000Z (about 11 years ago)
• Default Branch: master
• Last Pushed: 2024-01-17T18:22:40.000Z (10 months ago)
• Last Synced: 2024-07-24T11:49:11.063Z (4 months ago)
• Language: Julia
• Size: 121 KB
• Stars: 96
• Watchers: 9
• Forks: 34
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-sciml - JuliaStats/Loess.jl: Local regression, so smooooth!
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README

        
# Loess

[![CI](https://github.com/JuliaStats/Loess.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/JuliaStats/Loess.jl/actions/workflows/ci.yml)

This is a pure Julia loess implementation, based on the fast kd-tree based

approximation described in the original Cleveland, et al papers[1,2,3], implemented

in the netlib loess C/Fortran code, and used by many, including in R's loess

function.

## Synopsis

`Loess` exports two functions, `loess` and `predict`, that train and apply the model, respectively. The amount of smoothing is mainly controlled by the `span` keyword argument. E.g.:

```julia

using Loess, Plots

xs = 10 .* rand(100)

ys = sin.(xs) .+ 0.5 * rand(100)

model = loess(xs, ys, span=0.5)

us = range(extrema(xs)...; step = 0.1)

vs = predict(model, us)

scatter(xs, ys)

plot!(us, vs, legend=false)

```

![Example Plot](loess.svg)

There's also a shortcut in Gadfly to draw these plots:

```julia

plot(x=xs, y=ys, Geom.point, Geom.smooth, Guide.xlabel("x"), Guide.ylabel("y"))

```

## Status

Multivariate regression is not yet fully implemented, but most of the parts

are already there, and wouldn't require too much additional work.

## References

[1] Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American statistical association, 74(368), 829-836. DOI: 10.1080/01621459.1979.10481038

[2] Cleveland, W. S., & Devlin, S. J. (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American statistical association, 83(403), 596-610. DOI: 10.1080/01621459.1988.10478639

[3] Cleveland, W. S., & Grosse, E. (1991). Computational methods for local regression. Statistics and computing, 1(1), 47-62. DOI: 10.1007/BF01890836