evalGenHZ: Evaluate Generalized Horizon Labels

In aqp: Algorithms for Quantitative Pedology

Evaluate Generalized Horizon Labels

Description

Data-driven evaluation of generalized horizon labels using nMDS and silhouette width.

Usage

evalGenHZ(
  obj,
  genhz = GHL(obj, required = TRUE),
  vars,
  non.matching.code = "not-used",
  stand = TRUE,
  trace = FALSE,
  metric = "euclidean"
)

Arguments

obj	a SoilProfileCollection object
genhz	name of horizon-level attribute containing generalized horizon labels
vars	character vector of horizon-level attributes to include in the evaluation
non.matching.code	code used to represent horizons not assigned a generalized horizon label
stand	standardize variables before computing distance matrix, passed to cluster::daisy()
trace	verbose output from passed to MASS::isoMDS()
metric	distance metric, passed to cluster::daisy()

Details

Non-metric multidimensional scaling is performed via MASS::isoMDS(). The input distance matrix is generated by cluster::daisy() using (complete cases of) horizon-level attributes from obj as named in vars.

Silhouette widths are computed via cluster::silhouette(). The input distance matrix is generated by cluster::daisy() using (complete cases of) horizon-level attributes from obj as named in vars. Note that observations with genhz labels specified in non.matching.code are removed filtered before calculation of the distance matrix.

Value

a list is returned containing:

• horizons: ⁠c('mds.1', mds.2', 'sil.width', 'neighbor')⁠

• stats: mean and standard deviation vars, computed by generalized horizon label

• dist: the distance matrix as passed to MASS::isoMDS()

Author(s)

D.E. Beaudette

See Also

get.ml.hz()

aqp documentation built on Oct. 19, 2024, 5:06 p.m.