Undertakes an isometric log-ratio transformation to remove the effects of closure in a data matrix.
ilr(xx, ifclose = FALSE, ifwarn = TRUE)
a n
by p
matrix to be isometrically log-ratio transformed. It is essential that a single unit of measurement is used. Thus it may be required to convert, for example, determinations in percent to ppm (mg/kg) so that all measurements are in ppm prior to executing this function. Natural logarithms are used.
if it is required to close a data set prior to transformation set ifclose = TRUE
.
by default ifwarn = TRUE
which generates a reminder/warning that when carrying out a centred log-ratio transformation all the data must be in the same measurement units. The message can be suppressed by setting ifwarn = FALSE
.
a n
by (p-1)
matrix of isometric log-ratio values. The names of the new (p-1) synthetic variables, iso1
through to isop
, where the p
in isop
equals p-1
, are entered as column names in the matrix.
Most analytical chemical data for major, minor and trace elements are of a closed form, i.e. for a physical individual sample they sum to a constant, whether it be percent, ppm (mg/kg), or some other units. It does not matter that only some components contributing to the constant sum are present in the matrix, the data are closed. As a result, as some elements increase in concentration others must decrease, this leads to correlation measures and graphical presentations that do not reflect the true underlying relationships. However, isometrically transformed data matrices are not suitable for univariate EDA inspection as the new synthetic variables bear a complex relationship to the original measurements. For univariate studies use function gx.ilr
, and for bivariate studies use gx.symm.coords
Other procedures for removing closure effects are additive log-ratios (alr
) and centred log-ratios (clr
).
Aitchison, J. and Egozcue, J.J., 2005. Compositional data analysis; where are we and where should we be heading. Mathematical Geology, 37(7):829-850.
Buccianti, A., Mateu-Figueras, G, and Pawlowsky-Glahn, V. (eds.), 2006. Compositional data analysis in the geosciences: from theory to practice. The Geological Society Publishing House, Bath, U.K. Special Publication 264, 224 p.
Filzmoser, P. and Hron, K., 2008. Outlier detection for compositional data using robust methods. Mathematical Geosciences, 40(3):234-248.
Filzmoser, P., Hron, K. and Reimann, C., 2009. Principal component analysis for compositional data with outliers. Environmetrics, 20(6):621-633.
Filzmoser, P., Hron, K., Reimann, C. and Garrett, R.G., 2009. Robust factor analysis for compositional data. Computers & Geosciences, 35(9):1854-1861.
# NOT RUN {
## Make test data sind available
data(sind.mat2open)
## Undertake ilr transform
temp <- ilr(sind.mat2open)
temp
## Clean-up
rm(temp)
# }
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