aqp (version 1.0)

random_profile: Random Profile

Description

Generate a random soil profile according to set criteria, with correlated depth trends.

Usage

random_profile(id, n = c(3, 4, 5, 6), min_thick = 5,
 max_thick = 30, n_prop = 5, exact = FALSE, method = 'random_walk', ...)

Arguments

id

a character or numeric id used for this profile

n

vector of possible number of horizons, or the exact number of horizons (see below)

min_thick

minimum thickness criteria for a simulated horizon

max_thick

maximum thickness criteria for a simulated horizon

n_prop

number of simulated soil properties (columns in the returned dataframe)

exact

should the exact number of requested horizons be generated? (defaults to FALSE)

method

named method used to synthesize depth function ('random_walk' or 'LPP'), see details

...

additional parameters passed-in to the LPP (.lpp) function

Value

• A dataframe with the simulated profile.

Details

The logistic power peak (LPP) function can be used to generate random soil property depth functions that are sharply peaked. LPP parameters can be hard-coded using the optional arguments: "lpp.a", "lpp.b", "lpp.u", "lpp.d", "lpp.e". Amplitude of the peak is controlled by ("lpp.a + "lpp.b"), depth of the peak by "lpp.u", and abruptness by "lpp.d" and "lpp.e". Further description of the method is outlined in (Brenton et al, 2011).

References

Myers, D. B.; Kitchen, N. R.; Sudduth, K. A.; Miles, R. J.; Sadler, E. J. & Grunwald, S. Peak functions for modeling high resolution soil profile data Geoderma, 2011, 166, 74-83.

See Also

profile_compare

Examples

# generate 10 random profiles with default settings:
d <- ldply(1:10, random_profile)

# add a fake color
d$soil_color <- 'white'

# promote to SoilProfileCollection and plot
depths(d) <- id ~ top + bottom
plot(d)


# make a more interesting color based on the first property
# depth functions are generated using the LPP function
if(require(scales)) {
  opar <- par(mfrow=c(2,1))
  
  # setup color palette and mapping function
  cols <- rev(brewer_pal(pal='Spectral')(8))
  grad <- gradient_n_pal(cols)
  
  # generate data and color scales
  d <- ldply(1:10, random_profile, n=c(6, 7, 8), n_prop=1, method='LPP')
  d$soil_color <- cscale(d$p1, grad)
  
  # promote to SPC and plot
  depths(d) <- id ~ top + bottom
  plot(d)
  legend('bottom', legend=pretty(d$p1), pt.bg=cscale(pretty(d$p1), grad),
  pch=22, pt.cex=2.5, bty='n', horiz=TRUE)
  
  
  # do this again, this time set all of the LPP parameters
  d <- ldply(1:10, random_profile, n=c(6, 7, 8), n_prop=1, method='LPP', 
  lpp.a=5, lpp.b=10, lpp.d=5, lpp.e=5, lpp.u=25)
  d$soil_color <- cscale(d$p1, grad)
  depths(d) <- id ~ top + bottom
  plot(d)
  legend('bottom', legend=pretty(d$p1), pt.bg=cscale(pretty(d$p1), grad),
  pch=22, pt.cex=2.5, bty='n', horiz=TRUE)
  
  par(opar)
  }


# try plotting the LPP-derived simulated data
# aggregated over all profiles
a <- slab(d, fm= ~ p1)
a$mid <- with(a, (top + bottom) / 2)

(p1 <- xyplot(mid ~ p.q50, data=a,
lower=a$p.q25, upper=a$p.q75, ylim=c(150,-5), alpha=0.5,
panel=panel.depth_function, prepanel=prepanel.depth_function,
cf=a$contributing_fraction, xlab='Simulated Data', ylab='Depth',
main='LPP(a=5, b=10, d=5, e=5, u=25)',
par.settings=list(superpose.line=list(col='black', lwd=2))
))

# optionally add original data as step-functions
if(require(latticeExtra)) {
  h <- horizons(d)
  h$mid <- with(h, (top + bottom) / 2)
  p1 + as.layer(xyplot(top ~ p1, groups=id, data=h,
  horizontal=TRUE, type='S',
  par.settings=list(superpose.line=list(col='blue', lwd=1, lty=2))))
  }


# stress-test profile comparison functions (not run)
# d <- ldply(1:1000, random_profile)
# 
# 100 profiles, 4 varibales:
# 66 seconds on 1.3 Ghz Intel Mac Mini 
# D matrix = 192.3 Mb
# 768 Mb required
# p <- profile_compare(d, vars=c('p1','p2','p3','p4','p5'), max_d=50, k=0)

# more efficient computation, at the expense of precision, with 
# p <- profile_compare(d, vars=c('p1','p2','p3','p4','p5'),
# max_d=50, k=0, sample_interval=10)