Description Usage Arguments Examples
This method is analogous to loess
but it first finely bins the
data. This yields a substantially performance improvement (<1s for 10m
points), while only worsening performance slightly.
1 2 | compute_smooth_vec(x, z, span = 0.25, n_bin = 1000, n_smooth = 100,
weight = NULL)
|
x, z |
Numeric vectors. |
span |
Fraction of data that should be used by the smoother. Will be weighted by distance from predicted point. |
n_bin, n_smooth |
Number of components to use for binning and for smoothing. |
weight |
Optional. A numeric vector giving a weight for each location. |
1 2 3 4 5 6 7 8 | x <- runif(1e4, 0, 4 * pi)
y <- sin(x) + runif(1e4, -0.5, 0.5)
plot(x, y)
smu <- compute_smooth_vec(x, y, span = 0.25)
lines(smu$x, smu$y, type = "l", col = "red", lwd = 2)
x_grid <- seq(0, 4 * pi, length = 100)
lines(x_grid, sin(x_grid), type = "l", col = "blue", lwd = 2)
lines(x_grid, predict(loess(y ~ x), data.frame(x = x_grid)), col = "green", lwd = 2)
|
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