context("NCSP algorithm")
data("jacobs2000")
x <- jacobs2000
test_that('error conditions', {
# x must be a SPC
expect_error(
NCSP(3L, vars = c('sand', 'clay'))
)
# vars must exist in site / horizon
expect_error(
NCSP(x, vars = c('sand', 'tacos'))
)
# depthSequence must be reasonable
expect_error(
NCSP(x, vars = c('sand', 'clay'), maxDepth = -5)
)
})
# setup horizon-level data: data are from lab sampled pedons
d <- read.csv(
textConnection('series,top,bottom,clay,frags,ph
auburn,0,3,21,6,5.6
auburn,3,15,21,13,5.6
auburn,15,25,20,9,5.8
auburn,25,47,21,28,5.8
dunstone,0,5,16,13,6
dunstone,5,17,17,19,6.3
dunstone,17,31,20,6,6.3
dunstone,31,41,21,15,6.3
sobrante,0,5,18,0,5.8
sobrante,5,10,16,2,5.7
sobrante,10,28,15,21,5.8
sobrante,28,51,18,13,6.2
sobrante,51,74,20,12,6.2')
)
# establish site-level data
s <- data.frame(
series = c('auburn', 'dunstone', 'sobrante'),
precip = c(24, 30, 32)
)
# generate fake horizon names with clay / frags / ph
d$name <- with(d, paste(clay, frags, ph, sep='/'))
# upgrade to SoilProfile Collection object
depths(d) <- series ~ top + bottom
site(d) <- s
hzdesgnname(d) <- 'name'
## tests
test_that("NCSP works as expected", {
# compute between-profile dissimilarity, no depth weighting
d.dis <- suppressMessages(NCSP(d, vars = c('clay', 'ph', 'frags'), k = 0, maxDepth = 61))
m <- as.matrix(d.dis)
# results should be 3x3 distance matrix
# this is a cluster package object
expect_true(inherits(d.dis, c('dissimilarity', 'dist')))
expect_equal(dim(m), c(3, 3))
## TODO: manually check these
# known output
expect_equivalent(diag(m), c(0, 0, 0))
expect_equal(m[1,2], 33.03225, tolerance=0.00001)
expect_equal(m[1,3], 49.02092, tolerance=0.00001)
expect_equal(m[2,3], 45.94683, tolerance=0.00001)
})
test_that(".NCSP_distanceCalc() with color data", {
# color for 3 profiles
m <- c('10YR 2/2', '5YR 6/8', '5PB 8/2')
# # manually check dE00
# mm <- t(combn(m, 2))
# colorContrast(mm[, 1], mm[, 2])
# convert to CIELAB
.lab <- parseMunsell(m, returnLAB = TRUE)
# profile IDs
row.names(.lab) <- letters[1:nrow(.lab)]
# single soil matrix record
sm <- rep(TRUE, times = nrow(.lab))
# CIE2000 color contrast
d <- .NCSP_distanceCalc(.lab, sm = sm, isColor = TRUE)
# results should be 3x3 distance matrix
# not created by cluster package, plain old dist object
expect_true(inherits(d, 'dist'))
# convert to full matrix representation for eval
d <- as.matrix(d)
expect_equal(dim(d), c(3, 3))
# known output
expect_equivalent(diag(d), c(0, 0, 0))
expect_equal(d[1,2], 40.458, tolerance = 0.001)
expect_equal(d[1,3], 63.330, tolerance = 0.001)
expect_equal(d[2,3], 32.019, tolerance = 0.001)
})
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