Solve for a good set of right-exclusive x-cuts such that the
overall graph of y~x is well-approximated by a piecewise linear
function. Solution is a ready for use with
with base::findInterval()
and stats::approx()
(demonstrated in the examples).
solve_for_partitionc(
x,
y,
...,
w = NULL,
penalty = 0,
min_n_to_chunk = 1000,
min_seg = 1,
max_k = length(x)
)
numeric, input variable (no NAs).
numeric, result variable (no NAs, same length as x).
not used, force later arguments by name.
numeric, weights (no NAs, positive, same length as x).
per-segment cost penalty.
minimum n to subdivied problem.
positive integer, minimum segment size.
maximum segments to divide into.
a data frame appropriate for stats::approx().
# example data
d <- data.frame(
x = 1:8,
y = c(-1, -1, -1, -1, 1, 1, 1, 1))
# solve for break points
soln <- solve_for_partitionc(d$x, d$y)
# show solution
print(soln)
#> x pred group what
#> 1 1 -1 1 left
#> 2 2 -1 1 right
#> 3 3 -1 2 left
#> 4 4 -1 2 right
#> 5 5 1 3 left
#> 6 6 1 3 right
#> 7 7 1 4 left
#> 8 8 1 4 right
# label each point
d$group <- base::findInterval(
d$x,
soln$x[soln$what=='left'])
# apply piecewise approximation
d$estimate <- stats::approx(
soln$x,
soln$pred,
xout = d$x,
method = 'constant',
rule = 2)$y
# show result
print(d)
#> x y group estimate
#> 1 1 -1 1 -1
#> 2 2 -1 1 -1
#> 3 3 -1 2 -1
#> 4 4 -1 2 -1
#> 5 5 1 3 1
#> 6 6 1 3 1
#> 7 7 1 4 1
#> 8 8 1 4 1