Determine ML Horizon Boundaries
This function accepts input from slab() along with a vector of
horizon names, and returns a data.frame of the most likely horizon
boundaries.
get.ml.hz(x, o.names = attr(x, which = "original.levels"))
x |
output from |
o.names |
an optional character vector of horizon designations that will be used in the final table |
This function expects that x is a data.frame generated by
slab. If x was not generated by slab, then
o.names is required.
A dataframe with the following columns:
hz |
horizon names |
top |
top boundary |
bottom |
bottom boundary |
confidence |
integrated probability over thickness of each ML horizon, rounded to the nearest integer |
pseudo.brier |
A "pseudo"" Brier Score for a multi-class prediction, where the most-likely horizon label is treated as the "correct" outcome. Details on the calculation for traditional Brier Scores here: https://en.wikipedia.org/wiki/Brier_score. Lower values suggest better agreement between ML horizon label and class-wise probabilities. |
mean.H |
mean Shannon entropy (bits), derived from probabilities within each most-likely horizon. Larger values suggest more confusion within each ML. |
D.E. Beaudette
data(sp1)
depths(sp1) <- id ~ top + bottom
# normalize horizon names: result is a factor
sp1$name <- generalize.hz(sp1$name,
new=c('O','A','B','C'),
pat=c('O', '^A','^B','C'))
# compute slice-wise probability so that it sums to contributing fraction, from 0-150
a <- slab(sp1, fm= ~ name, cpm=1, slab.structure=0:150)
# generate table of ML horizonation
get.ml.hz(a)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.