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_partition(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, 2, 3, 4, 4, 3, 2, 1)) # solve for break points soln <- solve_for_partition(d$x, d$y) # show solution print(soln)#> x pred group what #> 1 1 1 1 left #> 2 4 4 1 right #> 3 5 4 2 left #> 4 8 1 2 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 = 'linear', rule = 2)$y # show result print(d)#> x y group estimate #> 1 1 1 1 1 #> 2 2 2 1 2 #> 3 3 3 1 3 #> 4 4 4 1 4 #> 5 5 4 2 4 #> 6 6 3 2 3 #> 7 7 2 2 2 #> 8 8 1 2 1