This function accepts input from `slab()`

(a `data.frame`

) along with a vector of
horizon names, and returns a `data.frame`

of the most likely horizon
boundaries.

This function expects that `x`

is a `data.frame`

generated by
`slab()`

. If `x`

was not generated by `slab()`

, then `o.names`

is required.

`get.ml.hz(x, o.names = attr(x, which = "original.levels"))`

- x
`data.frame`

, output from`slab()`

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

A `data.frame`

with the following columns:

`hz`

: horizon names`top`

: horizon top depth`bottom`

: horizon bottom depth`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.

Beaudette, D. E., Roudier, P., & Skovlin, J. (2016). Probabilistic representation of genetic soil horizons. Digital soil morphometrics, 281-293.

```
# init SPC
data(sp1)
depths(sp1) <- id ~ top + bottom
# set horizon designation metadata
hzdesgnname(sp1) <- 'name'
# generalize horizon designations from character vector
# result is an ordered factor
sp1$genhz <- generalizeHz(
sp1$name,
new = c('O','A','B','C'),
pat = c('O', '^A','^B','C'),
ordered = TRUE
)
# compute slice-wise GHL probability
# so that it sums to contributing fraction
# from 0-150cm
a <- slab(sp1, fm = ~ genhz, cpm = 1, slab.structure = 0:150)
#> horizons with zero thickness have been omitted from results
# note original GHL names are set by slab()
attr(a, 'original.levels')
#> [1] "O" "A" "B" "C" "not-used"
# 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
```