bootstrapSoilTexture {aqp} | R Documentation |

Simulate realistic sand/silt/clay values (a composition) using multivariate Normal distribution or Dirichlet distribution. Simulations from the multivariate Normal distribution are based on the compositional mean and variance-covariance matrix. Simulations from the Dirichlet distribution are based on maximum likelihood estimation of `alpha`

parameters.

bootstrapSoilTexture(ssc, method = c("dirichlet", "normal"), n = 100)

`ssc` |
a |

`method` |
type of simulation: |

`n` |
number of simulated compositions. See details. |

Simulations from the multivariate normal distribution will more closely track the marginal distributions of sand, silt, and clay–possibly a better fit for "squished" compositions (TODO elaborate). However, these simulations can result in extreme (unlikely) estimates.

Simulations from the Dirichlet distribution will usually be a better fit (fewer extreme estimates) but require a fairly large number of records in `ssc`

(`n >= 30`

?) for a reliable fit.

Additional examples will be added to this tutorial.

a `list`

containing:

`samples`

-`data.frame`

of simulated sand, silt, clay values`mean`

- compositional mean`var`

- compositional variance-covariance matrix`D.alpha`

- (fitted) alpha parameters of the Dirichlet distribution,`NULL`

when`method = 'normal'`

This is a work in progress.

D.E. Beaudette

Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman & Hall Ltd., London (UK). 416p.

Aitchison, J, C. Barcel'o-Vidal, J.J. Egozcue, V. Pawlowsky-Glahn (2002) A concise guide to the algebraic geometric structure of the simplex, the sample space for compositional data analysis, Terra Nostra, Schriften der Alfred Wegener-Stiftung, 03/2003

Malone Brendan, Searle Ross (2021) Updating the Australian digital soil texture mapping (Part 1*): re-calibration of field soil texture class centroids and description of a field soil texture conversion algorithm. Soil Research. https://www.publish.csiro.au/SR/SR20283

Malone Brendan, Searle Ross (2021) Updating the Australian digital soil texture mapping (Part 2*): spatial modelling of merged field and lab measurements. Soil Research. https://doi.org/10.1071/SR20284

if( requireNamespace("compositions") & requireNamespace("soiltexture") ) { # sample data, data.frame data('sp4') # filter just Bt horizon data ssc <- sp4[grep('^Bt', sp4$name), c('sand', 'silt', 'clay')] names(ssc) <- toupper(names(ssc)) # simulate 100 samples s <- bootstrapSoilTexture(ssc, n = 100) s <- s$samples # empty soil texture triangle TT <- soiltexture::TT.plot( class.sys= "USDA-NCSS.TT", main= "", tri.sum.tst=FALSE, cex.lab=0.75, cex.axis=0.75, frame.bg.col='white', class.lab.col='black', lwd.axis=1.5, arrows.show=TRUE, new.mar = c(3, 0, 0, 0) ) # add original data points soiltexture::TT.points( tri.data = s, geo = TT, col='firebrick', pch = 3, cex = 0.5, lwd = 1, tri.sum.tst = FALSE ) # add simulated points soiltexture::TT.points( tri.data = ssc, geo = TT, bg='royalblue', pch = 22, cex = 1, lwd = 1, tri.sum.tst = FALSE ) # simple legend legend('top', legend = c('Source', 'Simulated'), pch = c(22, 3), col = c('black', 'firebrick'), pt.bg = c('royalblue', NA), horiz = TRUE, bty = 'n' ) }

[Package *aqp* version 1.31 Index]