Popularity
82 Stars
Updated Last
12 Months Ago
Started In
September 2013

Loess

CI

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.:

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

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

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