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) )
| x | numeric, input variable (no NAs). |
|---|---|
| y | numeric, result variable (no NAs, same length as x). |
| ... | not used, force later arguments by name. |
| w | numeric, weights (no NAs, positive, same length as x). |
| penalty | per-segment cost penalty. |
| min_n_to_chunk | minimum n to subdivied problem. |
| min_seg | positive integer, minimum segment size. |
| max_k | 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