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

`slab`

- o.names
an optional character vector of horizon designations that will be used in the final table

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.

This function expects that `x`

is a data.frame generated by
`slab`

. If `x`

was not generated by `slab`

, then
`o.names`

is required.

```
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)
#> Note: aqp::slice() will be deprecated in aqp version 2.0
#> --> Please consider using the more efficient aqp::dice()
# generate table of ML horizonation
get.ml.hz(a)
#> hz top bottom confidence pseudo.brier mean.H
#> 1 O 0 2 37 0.3950617 0.9910761
#> 2 A 2 32 75 0.1547325 0.7922828
#> 3 B 32 145 57 0.3574667 1.0813045
#> 4 C 145 150 71 0.1250000 0.8112781
```