Evaluate Missing Data
Evaluate missing data in a SoilProfileCollection object
evalMissingData(x, vars, name = "hzname", p = "Cr|R|Cd", method = "relative")
x |
a |
vars |
a character vector naming horizon-level attributes in |
name |
the name of a horizon-level attribute where horizon designations are stored |
p |
REGEX pattern used to match non-soil horizons |
method |
'relative' (proportion of total) or 'absolute' depths |
Data completeness is evaluated by profile, based on the thickness of
horizons with complete horizon-level attribute values (specified in
vars) divided by the total thickness. The default REGEX pattern,
p, should catch most non-soil horizons which are excluded from the
evaluation.
A vector values ranging from 0 to 1 (method='relative') or 0
to maximum depth in specified depth units (method='absolute'),
representing the quantity of non-NA data (as specified in vars) for
each profile.
D.E. Beaudette
# example data
data(sp2)
# init SPC object
depths(sp2) <- id ~ top + bottom
# compute data completeness
sp2$data.complete <- evalMissingData(sp2, vars = c('r', 'g', 'b'), name = 'name')
sp2$data.complete.abs <- evalMissingData(sp2, vars = c('r', 'g', 'b'),
name = 'name', method = 'absolute')
# rank
new.order <- order(sp2$data.complete)
# plot along data completeness ranking
plot(sp2, plot.order=new.order, name='name')
# add relative completeness axis
# note re-ordering of axis labels
axis(side=1, at=1:length(sp2), labels = round(sp2$data.complete[new.order], 2),
line=-1.5, cex.axis=0.75)
# add absolute completeness (cm)
axis(side=1, at=1:length(sp2), labels = sp2$data.complete.abs[new.order],
line=1, cex.axis=0.75)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.