diff --git a/R/profileInformationIndex.R b/R/profileInformationIndex.R
index b81765ac9..247a0edc8 100644
--- a/R/profileInformationIndex.R
+++ b/R/profileInformationIndex.R
@@ -1,5 +1,5 @@
 
-# x: vector of any type
+# x: vector of any type, NA removed
 # d: number of significant digits to retain for numeric x
 .prepareVector <- function(x, d) {
   
@@ -31,7 +31,8 @@
   return(x)
 }
 
-
+# x: vector of any type, ignoring NA
+# d: number of significant digits to retain for numeric x
 .prepareBaseline <- function(x, d, type) {
   
   if(type == 'numeric') {
@@ -57,7 +58,7 @@
     # integer-coded (factor, logical)
     # frequency weighted by hz thickness, 
     # non-NA value
-    b <- names(sort(table(x), decreasing = TRUE))[1]
+    b <- names(sort(table(x, useNA = 'no'), decreasing = TRUE))[1]
     b <- rep(b, times = length(x))
   }
   
@@ -87,20 +88,25 @@
     x <- .prepareVector(x, d = d)
   }
   
+  # otherwise using a single string, with NA removed
+  
   # length of compressed vector is the metric
   res <- length(memCompress(x, type = m))
+  
+  # length of empty string ('') is 8 bits
+  res <- res - 8
+  
   return(res)
 }
 
 
-## TODO: think more about NA handling
 
 # i: vector, any type
 # numericDigits: requested precision for numeric data
-# removeNA: remove NA before preparing
-.prepareVariable <- function(i, numericDigits, removeNA) {
+.prepareVariable <- function(i, numericDigits) {
   
-  ## TODO: what is appropriate return value when all NA?
+  # remove all NA
+  i <- as.vector(na.omit(i))
   
   # short-circuit for all NA
   if(all(is.na(i))) {
@@ -109,10 +115,6 @@
   
   # baseline is mean(i)
   if(is.numeric(i)) {
-    # optionally remove NA from length(gz())
-    if(removeNA) {
-      i <- na.omit(i)
-    }
     
     # baseline for numeric data: 1cm slice wt. mean
     b <- .prepareBaseline(i, d = numericDigits, type = 'numeric')
@@ -124,11 +126,6 @@
     # categorical or logical
     # baseline is most-frequent, non-NA value
     
-    # optionally remove NA from length(gz())
-    if(removeNA) {
-      i <- na.omit(i)
-    }
-    
     # treating all categorical variables as nominal for now
     
     # baseline is the most frequent, weighted by hz thickness, non-NA value
@@ -143,7 +140,7 @@
 }
 
 
-.PII_by_profile <- function(x, vars, baseline, method, numericDigits, removeNA, compression) {
+.PII_by_profile <- function(x, vars, baseline, method, numericDigits, compression) {
   
   # diced SPC -> DF of horizon data
   # just variables of interest
@@ -153,8 +150,7 @@
   h <- lapply(
     h[, vars, drop = FALSE], 
     FUN = .prepareVariable, 
-    numericDigits = numericDigits,
-    removeNA = removeNA
+    numericDigits = numericDigits
   )
   
   # extract variables
@@ -219,9 +215,9 @@
 #' 
 #' @param numericDigits integer, number of significant digits to retain in numeric -> character conversion
 #' 
-#' @param removeNA remove NA before compression
+#' @param padNA logical, pad depths to `max(x)`, supplied to `dice(fill = padNA)`
 #' 
-#' @param padNA pad all profiles with NA to deepest lower depth in `x` (`max(x)`)
+#' @param scaleNumeric logical, `scale()` each numeric variable, causing "profile information" to vary based on other profiles in the collection
 #' 
 #' @param compression character, compression method as used by [memCompress()]: 'gzip', 'bzip2', 'xz', 'none'
 #'
@@ -293,29 +289,34 @@
 #' text(x = 1:5, y = 105, labels = pi, cex = 0.85)
 #' mtext('Profile Information Index (bytes)', side = 1, line = -1)
 #'
-profileInformationIndex <- function(x, vars, method = c('joint', 'individual'), baseline = FALSE, numericDigits = 8, removeNA = !padNA, padNA = FALSE, compression = 'gzip') {
+profileInformationIndex <- function(x, vars, method = c('joint', 'individual'), baseline = FALSE, numericDigits = 8, padNA = FALSE, scaleNumeric = FALSE, compression = 'gzip') {
   
   # sanity check
   method <- match.arg(method)
   
-  # scale numeric variables to mean of 0 and SD of 1
-  for(i in vars) {
-    if(inherits(x[[i]], 'numeric')) {
-      
-      # scale() will return NaN when SD() is NA or 0
-      .sd_i <- sd(x[[i]], na.rm = TRUE)
-      
-      # threshold for very small numbers 
-      if(!is.na(.sd_i)) {
-        if(.sd_i > 0.0001) {
-          x[[i]] <- scale(x[[i]])
+  ## TODO: think more about this
+  # scaling means that PII depends on collection and is not absolute
+  if(scaleNumeric) {
+    # scale numeric variables to mean of 0 and SD of 1
+    for(i in vars) {
+      if(inherits(x[[i]], 'numeric')) {
+        
+        # scale() will return NaN when SD() is NA or 0
+        .sd_i <- sd(x[[i]], na.rm = TRUE)
+        
+        # threshold for very small numbers 
+        if(!is.na(.sd_i)) {
+          if(.sd_i > 0.0001) {
+            x[[i]] <- scale(x[[i]])
+          }
         }
+        
+        # otherwise, use as-is
       }
-      
-      # otherwise, use as-is
     }
   }
   
+  
   ## TODO: this will error / drop profiles in the presence of bad horizonation
   
   # dice() to 1cm intervals for common baseline
@@ -339,7 +340,6 @@ profileInformationIndex <- function(x, vars, method = c('joint', 'individual'),
     baseline = baseline, 
     method = method,
     numericDigits = numericDigits, 
-    removeNA = removeNA, 
     compression = compression
   )
   
diff --git a/misc/sandbox/profile-information-index.R b/misc/sandbox/profile-information-index.R
index eeb3897bf..f413e6b4d 100644
--- a/misc/sandbox/profile-information-index.R
+++ b/misc/sandbox/profile-information-index.R
@@ -27,8 +27,8 @@ length(memCompress(a, type = 'gzip')) / length(memCompress(b, type = 'gzip'))
 
 
 aqp:::.prepareVector(1:10, d = 4)
-aqp:::.prepareVariable(1:10, numericDigits = 4, removeNA = FALSE)
-aqp:::.prepareVariable(c('A', 'A', 'A', 'B', 'C'), numericDigits = 4, removeNA = FALSE)
+aqp:::.prepareVariable(1:10, numericDigits = 4)
+aqp:::.prepareVariable(c('A', 'A', 'A', 'B', 'C'), numericDigits = 4)
 
 # note usage
 aqp:::.compressedLength(1:10)
@@ -44,6 +44,11 @@ aqp:::.compressedLength('A')
 aqp:::.compressedLength(factor('A'))
 
 
+# NA should not "add" information
+aqp:::.compressedLength(c('A', 'B', NA))
+aqp:::.compressedLength(c('A', 'B', 'C'))
+aqp:::.compressedLength(NA)
+
 aqp:::.compressedLength(0)
 aqp:::.compressedLength(1000)
 
@@ -137,7 +142,7 @@ p2 <- data.frame(
 p3 <- data.frame(
   id = 3, top = c(0, 10, 20, 30, 40, 50),
   bottom = c(10, 20, 30, 40, 50, 100),
-  p = c(1, 5, 10, 35, 6, 2),
+  p = c(5, 8, 10, 35, 6, 2),
   name = c('A1', 'A2', 'Bw', 'Bt1', 'Bt2', 'C')
 )
 
@@ -145,7 +150,7 @@ p3 <- data.frame(
 p4 <- data.frame(
   id = 4, top = c(0, 10, 20, 30, 40, 50),
   bottom = c(10, 20, 30, 40, 50, 100),
-  p = c(1, NA, NA, NA, NA, NA),
+  p = c(5, NA, NA, NA, NA, NA),
   name = c('A1', 'A2', 'Bw', 'Bt1', 'Bt2', 'C')
 )
 
@@ -194,7 +199,6 @@ mtext('Profile Complexity Ratio', side = 1, line = -0.5)
 
 
 
-
 # effect of aggregation function
 profileInformationIndex(z, vars = vars, method = 'i', baseline = TRUE)
 profileInformationIndex(z, vars = vars, method = 'j', baseline = TRUE)
@@ -203,14 +207,11 @@ profileInformationIndex(z, vars = vars, method = 'j', baseline = TRUE)
 profileInformationIndex(z, vars = vars, method = 'j', baseline = TRUE)
 profileInformationIndex(z, vars = vars, method = 'j', baseline = FALSE)
 
-# effect of removing NA
-profileInformationIndex(z, vars = vars, method = 'j', baseline = FALSE, removeNA = TRUE, padNA = FALSE)
-profileInformationIndex(z, vars = vars, method = 'j', baseline = FALSE, removeNA = FALSE, padNA = FALSE)
-
-
-profileInformationIndex(z, vars = vars, method = 'j', baseline = TRUE, padNA = TRUE, removeNA = FALSE)
-profileInformationIndex(z, vars = vars, method = 'j', baseline = TRUE, padNA = FALSE, removeNA = FALSE)
+# effect of padding depths, only when vars includes top/bottom
+profileInformationIndex(z, vars = c(vars, 'top'), method = 'j', baseline = FALSE, padNA = TRUE)
+profileInformationIndex(z, vars = c(vars, 'top'), method = 'j', baseline = FALSE, padNA = FALSE)
 
+# effect of number digits in character representation
 profileInformationIndex(z, vars = vars, method = 'j', baseline = TRUE, numericDigits = 1)
 profileInformationIndex(z, vars = vars, method = 'j', baseline = TRUE, numericDigits = 10)
 
@@ -265,31 +266,23 @@ x <- fetchOSD(s)
 
 # vars <- c('hue', 'value', 'chroma', 'texture_class', 'cf_class', 'pH', 'pH_class', 'distinctness', 'topography')
 
+
 par(mar = c(3, 0, 1, 2), mfrow = c(2, 1))
 
 vars <- c('hue', 'value', 'chroma', 'hzname')
 
-x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = FALSE, padNA = TRUE, removeNA = FALSE)
+x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = FALSE)
 
 plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
 axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('baseline = TRUE, removeNA = FALSE, padNA = TRUE')
+title('baseline = FALSE')
 
 
-x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = FALSE, padNA = FALSE, removeNA = TRUE)
+x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = TRUE)
 
 plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
 axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('baseline = TRUE, removeNA = TRUE, padNA = FALSE')
-
-
-
-x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = FALSE, padNA = FALSE, removeNA = TRUE)
-
-plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
-axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('baseline = FALSE, removeNA = FALSE, padNA = FALSE')
-
+title('baseline = TRUE')
 
 
 
@@ -300,69 +293,65 @@ par(mar = c(3, 0, 1, 2), mfrow = c(2, 1))
 
 vars <- c('hue', 'value', 'chroma', 'hzname')
 
-x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = FALSE, padNA = FALSE)
+x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = FALSE)
 
 plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
 axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('method = j, baseline = FALSE, padNA = FALSE', cex.main = 1)
+title('method = j, baseline = FALSE', cex.main = 1)
 
-x$pi <- profileInformationIndex(x, vars = vars, method = 'i', baseline = FALSE, padNA = FALSE)
+x$pi <- profileInformationIndex(x, vars = vars, method = 'i', baseline = FALSE)
 
 plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
 axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('method = i, baseline = FALSE, padNA = FALSE', cex.main = 1)
+title('method = i, baseline = FALSE', cex.main = 1)
 
 
-
-x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = FALSE, padNA = TRUE, removeNA = FALSE)
+x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = TRUE)
 
 plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
 axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('method = j, baseline = FALSE, padNA = TRUE', cex.main = 1)
+title('method = j, baseline = TRUE', cex.main = 1)
 
-x$pi <- profileInformationIndex(x, vars = vars, method = 'i', baseline = FALSE, padNA = TRUE, removeNA = FALSE)
+x$pi <- profileInformationIndex(x, vars = vars, method = 'i', baseline = TRUE)
 
 plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
 axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('method = i, baseline = FALSE, padNA = TRUE', cex.main = 1)
-
-dev.off()
-
+title('method = i, baseline = TRUE', cex.main = 1)
 
 
+# add top depth to test padNA = TRUE
+vars <- c('hue', 'value', 'chroma', 'hzname', 'top')
 
+x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = FALSE, padNA = TRUE)
 
+plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
+axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
+title('method = j, baseline = FALSE, padNA = TRUE', cex.main = 1)
 
-x$pi <- profileInformationIndex(x, vars = vars, baseline = TRUE, method = 'median')
+x$pi <- profileInformationIndex(x, vars = vars, method = 'i', baseline = FALSE, padNA = TRUE)
 
-par(mar = c(3, 0, 1, 2))
-plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE)
+plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
 axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('baseline = TRUE, method = median')
+title('method = i, baseline = FALSE, padNA = TRUE', cex.main = 1)
 
 
-x$pi <- profileInformationIndex(x, vars = vars, baseline = FALSE, method = 'sum')
+x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = TRUE, padNA = TRUE)
 
-par(mar = c(3, 0, 1, 2))
-plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE)
+plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
 axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('baseline = FALSE, method = sum')
-
+title('method = j, baseline = TRUE, padNA = TRUE', cex.main = 1)
 
-x$pi <- profileInformationIndex(x, vars = vars, baseline = TRUE, method = 'sum')
+x$pi <- profileInformationIndex(x, vars = vars, method = 'i', baseline = TRUE, padNA = TRUE)
 
-par(mar = c(3, 0, 1, 2))
-plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE)
+plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE, max.depth = 200)
 axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('baseline = TRUE, method = sum')
+title('method = i, baseline = TRUE, padNA = TRUE', cex.main = 1)
+
+
+dev.off()
 
 
-x$pi <- profileInformationIndex(x, vars = 'hue', baseline = TRUE, method = 'sum')
 
-par(mar = c(3, 0, 1, 2))
-plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE)
-axis(side = 1, at = 1:length(x), labels = format(x$pi, digits = 3)[order(x$pi)], cex.axis = 0.75, las = 1)
-title('baseline = TRUE, method = sum')
 
 
 
@@ -375,7 +364,7 @@ x$A <- .lab[, 2]
 x$B <- .lab[, 3]
 
 
-x$pi <- profileInformationIndex(x, vars = c('L', 'A', 'B'), baseline = FALSE, method = 'j', padNA = FALSE, removeNA = TRUE)
+x$pi <- profileInformationIndex(x, vars = c('L', 'A', 'B'), baseline = FALSE, method = 'j')
 
 par(mar = c(3, 0, 1, 2), mfrow = c(1,1))
 plotSPC(x, width = 0.3, name.style = 'center-center', plot.order = order(x$pi), cex.names = 0.66, shrink = TRUE)
@@ -420,10 +409,10 @@ vars <- c('hzname', 'hue', 'value', 'chroma', 'texture_class')
 
 
 z <- data.frame(
-  baseline.joint = profileInformationIndex(x, vars = vars, baseline = TRUE, method = 'j', padNA = FALSE, removeNA = TRUE),
-  baseline.individual = profileInformationIndex(x, vars = vars, baseline = TRUE, method = 'i', padNA = FALSE, removeNA = TRUE),
-  joint = profileInformationIndex(x, vars = vars, baseline = FALSE, method = 'j', padNA = FALSE, removeNA = TRUE),
-  individual = profileInformationIndex(x, vars = vars, baseline = FALSE, method = 'i', padNA = FALSE, removeNA = TRUE)
+  baseline.joint = profileInformationIndex(x, vars = vars, baseline = TRUE, method = 'j', padNA = FALSE),
+  baseline.individual = profileInformationIndex(x, vars = vars, baseline = TRUE, method = 'i', padNA = FALSE),
+  joint = profileInformationIndex(x, vars = vars, baseline = FALSE, method = 'j', padNA = FALSE),
+  individual = profileInformationIndex(x, vars = vars, baseline = FALSE, method = 'i', padNA = FALSE)
 )
 
 cor(z)
@@ -434,11 +423,21 @@ cor(z)
 hexplom(z, par.settings = tactile.theme(axis.text = list(cex = 0.66)), trans = log, inv = exp, xbins = 30, colramp = .cp, colorkey = FALSE, varname.cex = 0.75, varname.font = 2, main = 'Profile Information Index', xlab = '')
 
 
+## interesting... very little difference between joint vs. individual
+
+
 plot(joint ~ individual, data = z, las = 1)
 plot(baseline.joint ~ baseline.individual, data = z, las = 1)
 hexbinplot(baseline.joint ~ joint, data = z, par.settings = tactile.theme(), trans = log, inv = exp, xbins = 30, colramp = .cp, colorkey = FALSE, varname.cex = 0.75, varname.font = 2, main = 'Profile Information Index')
 
-x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = TRUE, padNA = FALSE, removeNA = TRUE)
+
+## Profile Complexity Ratio (baseline = TRUE): less influenced by number of horizons
+## --> cor ~ 0.44
+
+## Profile Complexity Index (baseline = FALSE): more influenced by number of horizons 
+## --> cor ~ 0.71
+
+x$pi <- profileInformationIndex(x, vars = vars, method = 'j', baseline = FALSE, padNA = FALSE)
 x$nhz <- profileApply(x, FUN = nrow, simplify = TRUE)
 
 x$greatgroup <- factor(x$greatgroup, levels = c('palexeralfs', 'haploxeralfs', 'haploxerepts', 'xerorthents', 'haploxererts', 'endoaquolls'))
@@ -504,7 +503,9 @@ site(z2)$g <- 'RW'
 
 z <- combine(z1, z2)
 
-z$pi <- profileInformationIndex(z, vars = c('p1', 'p2', 'p3'), method = 'j', baseline = FALSE, padNA = FALSE, removeNA = TRUE)
+## important: scales may be very different, scale() -> compress
+
+z$pi <- profileInformationIndex(z, vars = c('p1', 'p2', 'p3'), method = 'j', scaleNumeric = TRUE)
 z$nhz <- profileApply(z, FUN = nrow, simplify = TRUE)
 
 z$g <- factor(z$g)
@@ -515,6 +516,13 @@ z$nhz
 par(mar = c(0, 0, 3, 2))
 groupedProfilePlot(z, groups = 'g', color = 'p1')
 
+lsp <- get('last_spc_plot', envir = aqp.env)
+
+o <- lsp$plot.order
+.b <- z[, , .LAST, .BOTTOM]
+
+text(x = o, y = .b, labels = z$pi, cex = 0.66, pos = 1)
+
 
 bwplot(g ~ pi, data = site(z))
 
@@ -540,54 +548,16 @@ z1 <- lapply(
 z1 <- combine(z1)
 # z1 <- trunc(z1, 0, min(z1))
 
-z1$pi <- profileInformationIndex(z1, vars = c('p1', 'p2', 'p3'), method = 'j', baseline = TRUE, padNA = FALSE, removeNA = TRUE)
+z1$pi <- profileInformationIndex(z1, vars = c('p1', 'p2', 'p3'), method = 'j', scale = TRUE, baseline = TRUE, padNA = FALSE)
 
 par(mar = c(3, 0, 0, 2))
 plotSPC(z1, color = 'p1', plot.order = order(z1$pi), print.id = FALSE, width = 0.35, divide.hz = FALSE)
 axis(side = 1, at = 1:length(z1), labels = format(z1$pi[order(z1$pi)], digits = 3), cex.axis = 0.66)
 
 
-# simulate three profiles of increasing complexity
-p1 <- data.frame(id = 1, top = 0, bottom = 100, p = 5)
-p2 <- data.frame(id = 2, top = c(0, 10, 20, 30, 40, 50), bottom = c(10, 20, 30, 40, 50, 100), p = rep(5, times = 6))
-p3 <- data.frame(id = 3, top = c(0, 10, 20, 30, 40, 50), bottom = c(10, 20, 30, 40, 50, 100), p = c(1, 5, 10, 3, 6, 2))
-
-# combine and upgrade to SPC
-z <- rbind(p1, p2, p3)
-depths(z) <- id ~ top + bottom
-
-# visual check
-plotSPC(z, color = 'p')
-
-# compute information index several ways
-profileInformationIndex(z, vars = c('p'), method = 'sum')
-profileInformationIndex(z, vars = c('p'), method = 'mean')
-
-profileInformationIndex(z, vars = c('p'), method = 'mean', baseline = FALSE)
-profileInformationIndex(z, vars = c('p'), method = 'sum', baseline = FALSE)
-
-
-
-## ... need to resolve this
-# effect of profile depth
-p1 <- data.frame(id = 1, top = 0, bottom = 75, p = 5)
-p2 <- data.frame(id = 2, top = c(0, 10, 20, 30, 40, 50), bottom = c(10, 20, 30, 40, 50, 100), p = rep(5, times = 6))
-p3 <- data.frame(id = 3, top = c(0, 10, 20, 30, 40, 50), bottom = c(10, 20, 30, 40, 50, 150), p = c(1, 5, 10, 3, 6, 2))
-
-# combine and upgrade to SPC
-z <- rbind(p1, p2, p3)
-depths(z) <- id ~ top + bottom
-
-# visual check
-plotSPC(z, color = 'p')
-
-# compute information index several ways
-profileInformationIndex(z, vars = c('p'), method = 'sum')
-profileInformationIndex(z, vars = c('p'), method = 'mean')
-
-profileInformationIndex(z, vars = c('p'), method = 'mean', baseline = FALSE)
-profileInformationIndex(z, vars = c('p'), method = 'sum', baseline = FALSE)
-
-
+z1$pi <- profileInformationIndex(z1, vars = c('p1', 'p2', 'p3'), method = 'j', scale = TRUE, baseline = FALSE, padNA = FALSE)
 
+par(mar = c(3, 0, 0, 2))
+plotSPC(z1, color = 'p1', plot.order = order(z1$pi), print.id = FALSE, width = 0.35, divide.hz = FALSE)
+axis(side = 1, at = 1:length(z1), labels = format(z1$pi[order(z1$pi)], digits = 3), cex.axis = 0.66)