From f550cb7b31ad983f4378834bcfe248b928e6336f Mon Sep 17 00:00:00 2001 From: Beaudette Date: Wed, 13 Mar 2024 14:21:38 -0700 Subject: [PATCH] updates --- AQP/aqp/genhz-distance-eval.Rmd | 70 ++++-- AQP/aqp/genhz-distance-eval.html | 115 +++++---- AQP/aqp/investigating-soil-color.Rmd | 45 +++- AQP/aqp/investigating-soil-color.html | 238 +++++++++--------- ...racy-uncertainty-soil-class-prediction.Rmd | 8 +- 5 files changed, 266 insertions(+), 210 deletions(-) diff --git a/AQP/aqp/genhz-distance-eval.Rmd b/AQP/aqp/genhz-distance-eval.Rmd index 4a29d68..22c1b57 100644 --- a/AQP/aqp/genhz-distance-eval.Rmd +++ b/AQP/aqp/genhz-distance-eval.Rmd @@ -41,16 +41,16 @@ library(MASS) library(corrplot) +# example data data("loafercreek") hzdesgnname(loafercreek) <- 'hzname' - -# profiles 20-40 +# focus on profiles 20-40 x <- loafercreek[20:40, ] # modify default arguments for some flair par(mar = c(0, 0, 0, 0)) -plotSPC(x, width = 0.3, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, print.id = FALSE) +plotSPC(x, width = 0.3, name.style = 'center-center', depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, print.id = FALSE) ``` @@ -68,6 +68,7 @@ horizons(x)$genhz <- generalize.hz(x$hzname, new = n, pat = p) x$genhz <- ordered(x$genhz) # check frequency of GHL assignment +# note that some are missing table(x$genhz) ``` @@ -88,7 +89,7 @@ table(y$genhz) site(y)$shortID <- 1:length(y) par(mar = c(0, 0, 3, 0)) -plotSPC(y, color = 'genhz', width = 0.3, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, fixLabelCollisions = FALSE, col.label = 'Generalized Horizon Label', label = 'shortID') +plotSPC(y, color = 'genhz', width = 0.3, name.style = 'center-center', depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, fixLabelCollisions = FALSE, col.label = 'Generalized Horizon Label', label = 'shortID') ``` @@ -104,8 +105,8 @@ z$genhz[is.na(z$genhz)] <- 'R' # truncate all profiles to 150cm z <- trunc(z, z1 = 0, z2 = 150) -par(mar = c(0, 0, 3, 0)) -plotSPC(z, color = 'genhz', width = 0.3, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID') +par(mar = c(0, 0, 3, 2)) +plotSPC(z, color = 'genhz', width = 0.33, name.style = 'center-center', cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID') ``` @@ -115,15 +116,15 @@ Arrange profiles by depth to contact using `plot.order` argument to `plotSPC`. S sdc <- getSoilDepthClass(z) site(z) <- sdc -par(mar = c(0, 0, 3, 0)) -plotSPC(z, color = 'genhz', width = 0.3, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, plot.order = order(z$depth), col.label = 'Generalized Horizon Label', label = 'shortID') +par(mar = c(0, 0, 3, 2)) +plotSPC(z, color = 'genhz', width = 0.33, name.style = 'center-center', cex.names = 0.65, plot.order = order(z$depth), col.label = 'Generalized Horizon Label', label = 'shortID') ``` Compute pair-wise distances between profiles using `genhz` (an ordered factor) to a maximum depth of 150cm. See `?profile_compare` and `?daisy` for details. Divisive hierarchical clustering is applied to the distance matrix, with a final re-ordering step according to soil depth (`dendextend::rotate)`. ```{r} -# latest aqp -d <- NCSP(z, vars = c('genhz'), maxDepth = 150, k = 0, rescaleResult = TRUE) +# aqp >= 2.0 +d <- NCSP(z, vars = 'genhz', maxDepth = 150, k = 0, rescaleResult = TRUE) # divisive hierarchical clustering h <- as.hclust(diana(d)) @@ -134,13 +135,17 @@ h <- dendextend::rotate(h, order(z$depth)) As suggested by Shawn Salley, a quick view of the distance matrix as an image. Pair-wise distances are encoded with color. ```{r fig.width=8, fig.height=8} +# expand dist object to full matrix form of the pair-wise distances m <- as.matrix(d) +# copy short IDs from Soil Profile Collection to full distance matrix dimnames(m) <- list(z$shortID, z$shortID) +# invert device foreground / background colors for an artistic effect +# use colors from The Life Aquatic par(bg = 'black', fg = 'white') corrplot( m, - col = hcl.colors(n = 25), + col = hcl.colors(n = 25, palette = 'zissou1'), is.corr = FALSE, col.lim = c(0, 1), method = "color", @@ -155,13 +160,14 @@ corrplot( Rank, left to right, profiles according to median distance to all other profiles in the collection. Profiles 17, 6, 4, or 13 might be good candidates for typical pedons. ```{r, fig.width=12, fig.height=6} -# convert to matrix +# convert to full matrix representation m <- as.matrix(d) -# use shorter IDs +# copy shorter IDs from SPC dimnames(m) <- list(z$shortID, z$shortID) -# mask diagonal and lower triangle +# mask diagonal and lower triangle with NA +# ignoring these in subsequent steps m[upper.tri(m, diag = TRUE)] <- NA # extract distances to all other profiles @@ -170,25 +176,27 @@ dl <- lapply(1:nrow(m), FUN = function(i) { return(as.vector(na.omit(.r))) }) -# compute median distance to all other profiles +# copy names names(dl) <- dimnames(m)[[1]] + +# compute median distance to all other profiles (med.dist <- sort(sapply(dl, median, na.rm = TRUE))) # sorting vector for plotting sketches idx <- match(names(med.dist), z$shortID) -par(mar = c(4, 0, 3, 0)) -plotSPC(z, plot.order = idx, color = 'genhz', width = 0.25, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID') +par(mar = c(4, 0, 3, 2)) +plotSPC(z, plot.order = idx, color = 'genhz', width = 0.33, name.style = 'center-center', cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID') axis(1, at = 1:length(z), labels = round(med.dist, 3), line = 0, cex.axis = 0.85) mtext("Median distance to all other profiles (not to scale)", side = 1, line = 2.25) ``` -`plotProfileDendrogram` from the sharpshootR package aligns profile sketches with the terminal leaves of a dendrogram. +Use `plotProfileDendrogram()` from the sharpshootR package to align profile sketches with the terminal leaves of a dendrogram. When applied to other data, you may have to tinker with the `scaling.factor` and `y.offset` arguments. ```{r, fig.width=12, fig.height=8} -par(mar = c(1, 0, 3, 0)) -plotProfileDendrogram(z, clust = h, scaling.factor = 0.0095, y.offset = 0.15, color = 'genhz', width = 0.3, name.style = 'center-center', col.label = 'Generalized Horizon Label', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, label = 'shortID', fixLabelCollisions = TRUE) +par(mar = c(1, 0, 3, 2)) +plotProfileDendrogram(z, clust = h, scaling.factor = 0.0095, y.offset = 0.15, color = 'genhz', width = 0.33, name.style = 'center-center', col.label = 'Generalized Horizon Label', cex.names = 0.65, label = 'shortID') ``` @@ -202,9 +210,9 @@ pos <- alignTransect(m[1, ], x.min = 1, x.max = length(z), thresh = 0.5) Distance from 1st profile (left-most, marked with a red **asterisk**) used to order collection along an integer sequence. ```{r, fig.width=12, fig.height=6} -par(mar = c(4, 0, 3, 0)) +par(mar = c(4, 0, 3, 2)) -plotSPC(z, plot.order = pos$order, color = 'genhz', width = 0.25, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID') +plotSPC(z, plot.order = pos$order, color = 'genhz', width = 0.33, name.style = 'center-center', cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID') points(x = 1, y = -15, pch = '*', cex = 3, col = 'firebrick') @@ -214,7 +222,7 @@ mtext("Distance from 1st Profile (not to scale)", side = 1, line = 2.25) Distance from 1st profile (left-most, marked with a red **asterisk**) used to place profiles at relative distances along the x-axis. ```{r, fig.width=12, fig.height=6} -par(mar = c(4, 0, 3, 1)) +par(mar = c(4, 0, 3, 2)) plotSPC(z, plot.order = pos$order, relative.pos = pos$relative.pos, color = 'genhz', width = 0.2, name.style = 'center-center', name = NA, col.label = 'Generalized Horizon Label', label = 'shortID') @@ -227,7 +235,11 @@ mtext("Distance from 1st Profile", side = 1, line = 2.25) Create an ordination from pair-wise distances using non-metric multidimensional scaling (`MASS::sammon`). ```{r fig.width=8, fig.height=4.5} # add tiny amount for 0-distance pairs (duplicate profiles) -mds <- sammon(d + 0.001)$points +d <- d + 0.001 + +# extract nMDS scores from result +mds <- sammon(d) +mds <- mds$points # ensure ordering is preserved: # profile IDs -> distance matrix -> nMDS @@ -246,7 +258,8 @@ yoff <- aqp:::.rescaleRange(mds[, 2], x0 = -10, x1 = max(z)) Place ("hang") soil profile sketches according to nMDS ordination (points). Overlap is a problem. ```{r, fig.width=8, fig.height=6} par(mar = c(0.25, 0.25, 3.5, 0.25)) -plotSPC(z, y.offset = yoff, relative.pos = xoff, color = 'genhz', width = 0.25, name.style = 'center-center', hz.depths = FALSE, name = NA, col.label = 'Generalized Horizon Label', label = 'shortID', scaling.factor = 0.75) +# suppress horizon names with `names = NA` +plotSPC(z, y.offset = yoff, relative.pos = xoff, color = 'genhz', width = 0.25, name.style = 'center-center', name = NA, col.label = 'Generalized Horizon Label', label = 'shortID', scaling.factor = 0.75) points(xoff, yoff, pch = 16, cex = 0.66) @@ -256,7 +269,7 @@ abline(v = mean(xoff), h = mean(yoff), lty = 2) box() ``` -Demonstrate the effect of `fixOverlap` on relative positions along nMDS axis 1. +Demonstrate the effect of `fixOverlap()` on relative positions along nMDS axis 1. ```{r fig.width=6, fig.height=5} # use re-scaled nMDS coordinates as virtual transect # adjust to reduce overlap @@ -308,8 +321,11 @@ Another approach, leaving out all of the text annotation. Morphologic patterns a par(mar = c(0.25, 0.25, 3.5, 0.25)) plotSPC(z, y.offset = yoff, relative.pos = xoff.fixed, color = 'genhz', width = 0.25, name.style = 'center-center', hz.depths = FALSE, name = NA, col.label = 'Generalized Horizon Label', label = 'shortID', scaling.factor = 0.75) +# grid grid(nx = 10, ny = 10) -abline(v = mean(xoff.fixed), h = mean(yoff), lty = 2) + +# annotate origin of nMDS axes +points(x = mean(xoff), y = mean(yoff), pch = 3, col = 'black', lwd = 2) box() ``` diff --git a/AQP/aqp/genhz-distance-eval.html b/AQP/aqp/genhz-distance-eval.html index f902e74..af0e14e 100644 --- a/AQP/aqp/genhz-distance-eval.html +++ b/AQP/aqp/genhz-distance-eval.html @@ -11,7 +11,7 @@ - + Pair-Wise Distances by Generalized Horizon Labels @@ -354,7 +354,7 @@

Pair-Wise Distances by Generalized Horizon Labels

D.E. Beaudette

-

2023-08-07

+

2024-03-13

@@ -379,17 +379,17 @@

2023-08-07

library(corrplot) +# example data data("loafercreek") hzdesgnname(loafercreek) <- 'hzname' - -# profiles 20-40 +# focus on profiles 20-40 x <- loafercreek[20:40, ] # modify default arguments for some flair par(mar = c(0, 0, 0, 0)) -plotSPC(x, width = 0.3, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, print.id = FALSE) -

+plotSPC(x, width = 0.3, name.style = 'center-center', depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, print.id = FALSE) +

Apply generalized horizon labels using overly-simplistic rules. This is only a demonstration.

# new labels
@@ -404,6 +404,7 @@ 
2023-08-07

 x$genhz <- ordered(x$genhz)
 
 # check frequency of GHL assignment
+# note that some are missing
 table(x$genhz)
## 
 ##        O        A        B       Bt       BC       Cr        R not-used 
@@ -426,8 +427,8 @@ 
2023-08-07

 site(y)$shortID <- 1:length(y)
 
 par(mar = c(0, 0, 3, 0))
-plotSPC(y, color = 'genhz', width = 0.3, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, fixLabelCollisions = FALSE, col.label = 'Generalized Horizon Label', label = 'shortID')
-

+plotSPC(y, color = 'genhz', width = 0.3, name.style = 'center-center', depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, fixLabelCollisions = FALSE, col.label = 'Generalized Horizon Label', label = 'shortID') +

Add empty horizons to bottom of the profiles with bottom-most horizon depth < 150cm, label with ‘R’ GHL. Truncate deeper profiles to 150cm. See ?fillHzGaps and ?trunc for details.

@@ -441,9 +442,9 @@

2023-08-07

# truncate all profiles to 150cm z <- trunc(z, z1 = 0, z2 = 150) -par(mar = c(0, 0, 3, 0)) -plotSPC(z, color = 'genhz', width = 0.3, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID') -

+par(mar = c(0, 0, 3, 2)) +plotSPC(z, color = 'genhz', width = 0.33, name.style = 'center-center', cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID') +

Arrange profiles by depth to contact using plot.order argument to plotSPC. See ?getSoilDepthClass and ?estimateSoilDepth for details on estimating soil @@ -452,17 +453,17 @@

2023-08-07

sdc <- getSoilDepthClass(z) site(z) <- sdc -par(mar = c(0, 0, 3, 0)) -plotSPC(z, color = 'genhz', width = 0.3, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, plot.order = order(z$depth), col.label = 'Generalized Horizon Label', label = 'shortID') -

+par(mar = c(0, 0, 3, 2)) +plotSPC(z, color = 'genhz', width = 0.33, name.style = 'center-center', cex.names = 0.65, plot.order = order(z$depth), col.label = 'Generalized Horizon Label', label = 'shortID') +

Compute pair-wise distances between profiles using genhz (an ordered factor) to a maximum depth of 150cm. See ?profile_compare and ?daisy for details. Divisive hierarchical clustering is applied to the distance matrix, with a final re-ordering step according to soil depth (dendextend::rotate).

-
# latest aqp
-d <- NCSP(z, vars = c('genhz'), maxDepth = 150, k = 0, rescaleResult = TRUE)
+
# aqp >= 2.0
+d <- NCSP(z, vars = 'genhz', maxDepth = 150, k = 0, rescaleResult = TRUE)
 
 # divisive hierarchical clustering
 h <- as.hclust(diana(d))
@@ -471,13 +472,17 @@ 
2023-08-07

 h <- dendextend::rotate(h, order(z$depth))

 
As suggested by Shawn Salley, a quick view of the distance matrix as
 an image. Pair-wise distances are encoded with color.

-
m <- as.matrix(d)
+
# expand dist object to full matrix form of the pair-wise distances 
+m <- as.matrix(d)
+# copy short IDs from Soil Profile Collection to full distance matrix
 dimnames(m) <- list(z$shortID, z$shortID)
 
+# invert device foreground / background colors for an artistic effect
+# use colors from The Life Aquatic
 par(bg = 'black', fg = 'white')
 corrplot(
   m, 
-  col = hcl.colors(n = 25), 
+  col = hcl.colors(n = 25, palette = 'zissou1'), 
   is.corr = FALSE, 
   col.lim = c(0, 1), 
   method = "color", 
@@ -487,17 +492,18 @@ 
2023-08-07

   # cl.pos = "n",
   mar = c(0.1, 0, 0, 0.8)
 ) 

-

+

 
Rank, left to right, profiles according to median distance to all
 other profiles in the collection. Profiles 17, 6, 4, or 13 might be good
 candidates for typical pedons.

-
# convert to matrix
+
# convert to full matrix representation
 m <- as.matrix(d)
 
-# use shorter IDs
+# copy shorter IDs from SPC
 dimnames(m) <- list(z$shortID, z$shortID)
 
-# mask diagonal and lower triangle 
+# mask diagonal and lower triangle with NA
+# ignoring these in subsequent steps
 m[upper.tri(m, diag = TRUE)] <- NA
 
 # extract distances to all other profiles
@@ -506,8 +512,10 @@ 
2023-08-07

   return(as.vector(na.omit(.r)))
 })
 
-# compute median distance to all other profiles
+# copy names
 names(dl) <- dimnames(m)[[1]]
+
+# compute median distance to all other profiles
 (med.dist <- sort(sapply(dl, median, na.rm = TRUE)))

 
##        17         6         4        13        19        14         5         3        15         1 
 ## 0.2615694 0.2635815 0.2726358 0.2756539 0.2877264 0.2907445 0.3138833 0.3138833 0.3259557 0.3269618 
@@ -516,17 +524,19 @@ 
2023-08-07

 
# sorting vector for plotting sketches
 idx <- match(names(med.dist), z$shortID)
 
-par(mar = c(4, 0, 3, 0))
-plotSPC(z, plot.order = idx, color = 'genhz', width = 0.25, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID')
+par(mar = c(4, 0, 3, 2))
+plotSPC(z, plot.order = idx, color = 'genhz', width = 0.33, name.style = 'center-center', cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID')
 
 axis(1, at = 1:length(z), labels = round(med.dist, 3), line = 0, cex.axis = 0.85)
 mtext("Median distance to all other profiles (not to scale)", side = 1, line = 2.25)

-

-
plotProfileDendrogram from the sharpshootR package
-aligns profile sketches with the terminal leaves of a dendrogram.

-
par(mar = c(1, 0, 3, 0))
-plotProfileDendrogram(z, clust = h, scaling.factor = 0.0095, y.offset = 0.15, color = 'genhz', width = 0.3, name.style = 'center-center', col.label = 'Generalized Horizon Label', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, label = 'shortID', fixLabelCollisions = TRUE)

-

+

+
Use plotProfileDendrogram() from the sharpshootR package
+to align profile sketches with the terminal leaves of a dendrogram. When
+applied to other data, you may have to tinker with the
+scaling.factor and y.offset arguments.

+
par(mar = c(1, 0, 3, 2))
+plotProfileDendrogram(z, clust = h, scaling.factor = 0.0095, y.offset = 0.15, color = 'genhz', width = 0.33, name.style = 'center-center', col.label = 'Generalized Horizon Label', cex.names = 0.65, label = 'shortID')

+

 
Use distance from 1st profile as a virtual transect via
 alignTransect, fixOverlap attempts to
 spread-out profiles along the x-axis to reduce overlap.

@@ -537,19 +547,19 @@ 
2023-08-07

 
Distance from 1st profile (left-most, marked with a red
 asterisk) used to order collection along an integer
 sequence.

-
par(mar = c(4, 0, 3, 0))
+
par(mar = c(4, 0, 3, 2))
 
-plotSPC(z, plot.order = pos$order, color = 'genhz', width = 0.25, name.style = 'center-center', plot.depth.axis = FALSE, hz.depths = TRUE, cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID')
+plotSPC(z, plot.order = pos$order, color = 'genhz', width = 0.33, name.style = 'center-center', cex.names = 0.65, col.label = 'Generalized Horizon Label', label = 'shortID')
 
 points(x = 1, y = -15, pch = '*', cex = 3, col = 'firebrick')
 
 axis(1, at = 1:length(z), labels = round(pos$grad, 3), line = 0, cex.axis = 0.85)
 mtext("Distance from 1st Profile (not to scale)", side = 1, line = 2.25)

-

+

 
Distance from 1st profile (left-most, marked with a red
 asterisk) used to place profiles at relative distances
 along the x-axis.

-
par(mar = c(4, 0, 3, 1))
+
par(mar = c(4, 0, 3, 2))
 
 plotSPC(z, plot.order = pos$order, relative.pos = pos$relative.pos, color = 'genhz', width = 0.2, name.style = 'center-center', name = NA, col.label = 'Generalized Horizon Label', label = 'shortID')
 
@@ -557,16 +567,21 @@ 
2023-08-07

 
 axis(1, at = pos$relative.pos, labels = round(pos$grad, 3), line = 0, cex.axis = 0.66)
 mtext("Distance from 1st Profile", side = 1, line = 2.25)

-

+

 
Create an ordination from pair-wise distances using non-metric
 multidimensional scaling (MASS::sammon).

 
# add tiny amount for 0-distance pairs (duplicate profiles)
-mds <- sammon(d + 0.001)$points

+d <- d + 0.001
+
+# extract nMDS scores from result
+mds <- sammon(d)

 
## Initial stress        : 0.02655
-## stress after  10 iters: 0.00945, magic = 0.338
-## stress after  20 iters: 0.00906, magic = 0.500
+## stress after  10 iters: 0.00931, magic = 0.500
+## stress after  20 iters: 0.00908, magic = 0.500
 ## stress after  30 iters: 0.00905, magic = 0.500

-
# ensure ordering is preserved:
+
mds <- mds$points
+
+# ensure ordering is preserved:
 # profile IDs -> distance matrix -> nMDS
 all(row.names(mds) == profile_id(z))

 
## [1] TRUE

@@ -580,7 +595,8 @@ 
2023-08-07

 
Place (“hang”) soil profile sketches according to nMDS ordination
 (points). Overlap is a problem.

 
par(mar = c(0.25, 0.25, 3.5, 0.25))
-plotSPC(z, y.offset = yoff, relative.pos = xoff, color = 'genhz', width = 0.25, name.style = 'center-center', hz.depths = FALSE, name = NA, col.label = 'Generalized Horizon Label', label = 'shortID', scaling.factor = 0.75)
+# suppress horizon names with `names = NA`
+plotSPC(z, y.offset = yoff, relative.pos = xoff, color = 'genhz', width = 0.25, name.style = 'center-center', name = NA, col.label = 'Generalized Horizon Label', label = 'shortID', scaling.factor = 0.75)
 
 points(xoff, yoff, pch = 16, cex = 0.66)
 
@@ -588,8 +604,8 @@ 
2023-08-07

 abline(v = mean(xoff), h = mean(yoff), lty = 2)
 
 box()

-

-
Demonstrate the effect of fixOverlap on relative
+

+
Demonstrate the effect of fixOverlap() on relative
 positions along nMDS axis 1.

 
# use re-scaled nMDS coordinates as virtual transect
 # adjust to reduce overlap
@@ -623,7 +639,7 @@ 
2023-08-07

 
 legend('bottomleft', legend = c('original', 'adjusted'), pch = c(1, 16), box.col = 'white', text.font = c(3,2))
 box()

-

+

 
Place soil profile sketches according to approximate positions within
 nMDS ordination (points). Note that y-offsets (nMDS axis 2 are inverted,
 due to “0 is at the top” depth logic used by plotSPC()). A
@@ -635,19 +651,22 @@ 
2023-08-07

 points(xoff.fixed, yoff, pch = 16, cex = 0.75)
 
 box()

-

+

 
Another approach, leaving out all of the text annotation. Morphologic
 patterns and their similarity are clear.

 
par(mar = c(0.25, 0.25, 3.5, 0.25))
 plotSPC(z, y.offset = yoff, relative.pos = xoff.fixed, color = 'genhz', width = 0.25, name.style = 'center-center', hz.depths = FALSE, name = NA, col.label = 'Generalized Horizon Label', label = 'shortID', scaling.factor = 0.75)
 
+# grid
 grid(nx = 10, ny = 10)
-abline(v = mean(xoff.fixed), h = mean(yoff), lty = 2)
+
+# annotate origin of nMDS axes
+points(x = mean(xoff), y = mean(yoff), pch = 3, col = 'black', lwd = 2)
 
 box()

-

+

 

-
This document is based on aqp version 2.0.

+
This document is based on aqp version 2.0.3.

 
 
 
diff --git a/AQP/aqp/investigating-soil-color.Rmd b/AQP/aqp/investigating-soil-color.Rmd
index b16509e..8894625 100644
--- a/AQP/aqp/investigating-soil-color.Rmd
+++ b/AQP/aqp/investigating-soil-color.Rmd
@@ -25,7 +25,7 @@ options(width=100, stringsAsFactors=FALSE, cache=FALSE)
 ```{r}
 library(aqp)
 library(soilDB)
-library(plyr)
+# library(plyr)
 library(sharpshootR)
 library(latticeExtra)
 library(colorspace)
@@ -64,7 +64,13 @@ x <- fetchKSSL(series='clarksville', returnMorphologicData = TRUE, simplifyColor
 s <- x$SPC
 
 # genhz
-s$genhz <- generalize.hz(s$hzn_desgn, c('A', 'E', 'Bt', '2Bt', '3Bt'), pat=c('A', 'E', '^Bt', '2B', '3B'), non.matching.code = NA)
+s$genhz <- generalize.hz(
+  x = s$hzn_desgn, 
+  new = c('A', 'E', 'Bt', '2Bt', '3Bt'), 
+  pattern = c('A', 'E', '^Bt', '2B', '3B'),
+  non.matching.code = NA
+)
+
 s$genhz <- factor(s$genhz, levels = guessGenHzLevels(s, "genhz")$levels)
 
 table(s$genhz, useNA = 'always')
@@ -146,11 +152,11 @@ xyplot(m_value ~ m_chroma | m_hue, as.table=TRUE, data=h, subscripts = TRUE, xli
   tab <- prop.table(table(p.data$m, useNA = 'always'))
   tab <- as.data.frame(tab)
   names(tab) <- c('m', 'freq')
-  p.data <- join(p.data, tab, by='m', type='left')
+  p.data <- merge(p.data, tab, by = 'm', all.x = TRUE, sort = FALSE)
   p.data <- na.omit(p.data)
   p.data <- subset(p.data, subset=freq > 0.05)
   panel.grid(-1, -1)
-  panel.xyplot(p.data$x, p.data$y, pch=22, col='black', fill=p.data$col, cex=4 * sqrt(p.data$freq))
+  panel.xyplot(p.data$x, p.data$y, pch = 22, col = 'black', fill = p.data$col, cex = 4 * sqrt(p.data$freq))
 })
 
 
@@ -160,7 +166,7 @@ pp <- xyplot(m_value ~ m_chroma | m_hue + genhz, as.table=TRUE, data=h, subscrip
   tab <- prop.table(table(p.data$m, useNA = 'always'))
   tab <- as.data.frame(tab)
   names(tab) <- c('m', 'freq')
-  p.data <- join(p.data, tab, by='m', type='left')
+  p.data <- merge(p.data, tab, by = 'm', all.x = TRUE, sort = FALSE)
   p.data <- na.omit(p.data)
   p.data <- subset(p.data, subset=freq > 0.05)
   panel.grid(-1, -1)
@@ -175,14 +181,13 @@ useOuterStrips(pp, strip=strip.custom(bg=grey(0.85)), strip.left = strip.custom(
 # Soil Color Aggregation 
 ```{r}
 a <- aggregateColor(s, "genhz", col = 'moist_soil_color')
-a.reduced <- aggregateColor(s, "genhz", col = 'moist_soil_color', k=8)
+a.reduced <- aggregateColor(s, "genhz", col = 'moist_soil_color', k = 8)
 
 par(mar = c(4.5, 2.5, 4.5, 0))
 aggregateColorPlot(a, label.cex = 0.65, main = "Clarksville Moist Colors\nGeneralized Horizons", print.n.hz = FALSE, print.label = FALSE, rect.border = NA, horizontal.borders = TRUE)
 
 par(mar = c(4.5, 2.5, 4.5, 0))
 aggregateColorPlot(a.reduced, label.cex = 0.65, main = "Clarksville Moist Colors\nGeneralized Horizons\n8 Colors per Group", print.n.hz = TRUE)
-
 ```
 
 # Soil Color RIC via treemap
@@ -220,7 +225,7 @@ ggplot(data = a) +
 ```{r fig.width = 8.5, fig.height = 4}
 # simulation parameters
 p <- list(
-  list(m = '7.5YR 4/6', thresh = 10, hues = c('5YR', '7.5YR', '10YR'))
+  list(m = '7.5YR 4/6', thresh = 12, hues = c('5YR', '7.5YR', '10YR'))
 )
 
 # simulation
@@ -228,7 +233,29 @@ sim <- simulateColor(method = 'dE00', n = 100, parameters = p)
 
 # present via color chart / tabulation
 pp <- colorChart(sim[[1]], annotate = FALSE, chip.cex = 3)
-update(pp, asp = 1, main = 'RV Color: 7.5YR 4/6\ndE00 threshold < 8')
+update(pp, asp = 1, main = 'RV Color: 7.5YR 4/6\ndE00 threshold < 12')
+```
+
+
+```{r fig.width = 8.5, fig.height = 4}
+# extract horizons, to generate a data.frame of moist colors
+h <- horizons(s)
+
+# remove horizons that are missing moist colors
+idx <- which(complete.cases(h[, c('m_hue', 'm_value', 'm_chroma', 'genhz')]))
+h <- h[idx, ]
+
+# simulation parameters
+p <- list(
+  list(hvc = data.frame(hue = h$m_hue, value = h$m_value, chroma = h$m_chroma))
+)
+
+# simulation
+sim <- simulateColor(method = 'mvnorm', n = 100, parameters = p)
+
+# present via color chart / tabulation
+pp <- colorChart(sim[[1]], annotate = FALSE, chip.cex = 3)
+update(pp, main = 'Multivariate Simulation (All Colors)')
 ```
 
 
diff --git a/AQP/aqp/investigating-soil-color.html b/AQP/aqp/investigating-soil-color.html
index 0e873da..27cb61b 100644
--- a/AQP/aqp/investigating-soil-color.html
+++ b/AQP/aqp/investigating-soil-color.html
@@ -11,7 +11,7 @@
 
 
 
-
+
 
 Investigating Soil Color
 
@@ -32,7 +32,7 @@
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