Description Usage Arguments Details Value References See Also Examples
For use in model formulas,
natural cubic spline as in splines::ns
but with knot positions chosen using
k-means rather than quantiles.
Automatically uses less knots if there are insufficient distinct values.
1 | well_knotted_spline(x, n_knots, verbose = TRUE)
|
x |
The predictor variable. A numeric vector. |
n_knots |
Number of knots to use. |
verbose |
If TRUE, produce a message about the knots chosen. |
Wong (1982, 1984) showed the asymptotic density of k-means in 1 dimension is
proportional to the cube root of the density of x.
Compared to using quantiles (the default for ns
),
choosing knots using k-means produces a better spread of knot locations
if the distribution of values is very uneven.
k-means is computed in an optimal, deterministic way using
Ckmeans.1d.dp
.
A matrix of predictors, similar to ns
.
This function supports "safe prediction"
(see makepredictcall
).
Original knot locations will be used for prediction with
predict
.
Wong, M. (1982). Asymptotic properties of univariate sample k-means clusters. Working paper #1341-82, Sloan School of Management, MIT. https://dspace.mit.edu/handle/1721.1/46876
Wong, M. (1984). Asymptotic properties of univariate sample k-means clusters. Journal of Classification, 1(1), 255<e2><80><93>270. https://doi.org/10.1007/BF01890126
1 2 3 4 5 6 7 8 9 | lm(mpg ~ well_knotted_spline(wt,3), data=mtcars)
# When insufficient unique values exist, less knots are used
lm(mpg ~ well_knotted_spline(gear,3), data=mtcars)
library(ggplot2)
ggplot(diamonds, aes(carat, price)) +
geom_point() +
geom_smooth(method="lm", formula=y~well_knotted_spline(x,10))
|
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