sharpshootR (version 1.5)

SoilTaxonomyDendrogram: Soil Taxonomy Dendrogram

Description

Plot a dendrogram based on the first 4 levels of Soil Taxonomy, with soil profiles hanging below. A dissimmilarity matrix is computed using Gower's distance metric for nominal-scale variables, based on order, sub order, great group, and subgroup level taxa. See the Details and Examples sections below for more information.

Usage

SoilTaxonomyDendrogram(spc, name = "hzname", max.depth = 150, 
n.depth.ticks = 6, scaling.factor = 0.015, cex.names = 0.75, 
cex.id = 0.75, axis.line.offset = -4, width = 0.1, y.offset = 0.5, 
shrink=FALSE, font.id=2,
cex.taxon.labels = 0.66, dend.color = par("fg"), dend.width = 1)

Arguments

spc

a SoilProfileCollection object, see details

name

column name containing horizon names

max.depth

depth at which profiles are truncated for plotting

n.depth.ticks

suggested number of ticks on the depth axis

scaling.factor

scaling factor used to convert depth units into plotting units

cex.names

character scaling for horizon names

cex.id

character scaling for profile IDs

axis.line.offset

horizontal offset for depth axis

width

width of profiles

y.offset

vertical offset between dendrogram and profiles

shrink

should long horizon names be shrunk by 80% ?

font.id

font style applied to profile id, default is 2 (bold)

cex.taxon.labels

character scaling for taxonomic information

dend.color

dendrogram line color

dend.width

dendrogram line width

Value

An invisibly-returned list containing:

dist

pair-wise dissimilarity matrix

Details

This function looks for specific site-level attributes named: soilorder, suborder, greatgroup, and subgroup.

References

D.E. Beaudette, P. Roudier and A.T. O'Geen. 2012. Algorithms for Quantitative Pedology, a Toolkit for Soil Scientists. Computers & Geosciences: 52: 258--268. (doi: 10.1016/j.cageo.2012.10.020)

Examples

# NOT RUN {
library(soilDB)

# soils of interest
s.list <- c('musick', 'cecil', 'drummer', 'amador', 'pentz', 'reiff', 
'san joaquin','montpellier','grangeville','pollasky','ramona')

# fetch and convert data into an SPC
h <- fetchOSD(s.list)

# plot dendrogram + profiles
SoilTaxonomyDendrogram(h)

# again, this time save the pair-wise dissimilarity matrix
# note that there isn't a lot of discrimination between soils
(d <- SoilTaxonomyDendrogram(h))
# }