# Loess.jl

**Repository Path**: Julialang/Loess.jl

## Basic Information

- **Project Name**: Loess.jl
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-03-12
- **Last Updated**: 2024-07-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Loess

[![Build Status](https://travis-ci.org/JuliaStats/Loess.jl.svg?branch=master)](https://travis-ci.org/JuliaStats/Loess.jl)
[![Loess](http://pkg.julialang.org/badges/Loess_0.5.svg)](http://pkg.julialang.org/?pkg=Loess)
[![Loess](http://pkg.julialang.org/badges/Loess_0.6.svg)](http://pkg.julialang.org/?pkg=Loess)

This is a pure Julia loess implementation, based on the fast kd-tree based
approximation described in the original Cleveland, et al papers, 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.


```julia
using Loess

xs = 10 .* rand(100)
ys = sin(xs) .+ 0.5 * rand(100)

model = loess(xs, ys)

us = collect(minimum(xs):0.1:maximum(xs))
vs = predict(model, us)

using Gadfly
p = plot(x=xs, y=ys, Geom.point, Guide.xlabel("x"), Guide.ylabel("y"),
         layer(Geom.line, x=us, y=vs))
draw(SVG("loess.svg", 6inch, 3inch), p)
```

![Example Plot](http://JuliaStats.github.io/Loess.jl/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.