robust.scores: Extract robust end-member scores.

Description

This function takes a list object with statistics of end-member loadings and propagates these uncertainties to end-member scores using Monte Carlo methods.

Usage

robust.scores(loadings, l, mc_n, cores = 1, plot = FALSE, ...)

Value

List with statistic descriptions of robust end-member scores.

Arguments

loadings: List of class "EMMAgeo_robload", i.e. the outout of robust.loadings(), containing statistic descriptions of robust end-member loadings.
l: Numeric scalar, weight transformation limit to use for modelling the average end-member output. Can be output of get.l.opt(). If omitted, it is set to 0.
mc_n: Numeric scalar, number of Monte Carlo simulations to estimate end-member scores uncertainty. The default setting is ten times the product of number of end-members and number of weight transformation limits. The latter is inherited from model.em(). To disable modelling of scores uncertainty, set mc_n = 0.
cores: Numeric scalar, number of CPU cores to be used for calculations. Only useful in multicore architectures. Default is 1 (single core).
plot: Logical scalar, option for plot output. Default is FALSE.
...: Additional arguments passed to EMMA and plot.

Author

Michael Dietze, Elisabeth Dietze

Examples

Run this code


## load example data set, potential end-members, output of model.EM()
data(example_EMpot)

## define limits for robust end-members
limits <- cbind(c(61, 74, 95, 102), 
                c(64, 76, 100, 105))

## get robust end-member loadings
robust_loadings <- robust.loadings(em = EMpot, limits = limits)

## model end-member scores uncertainties with minimum Monte Carlo runs
robust_scores <- robust.scores(loadings = robust_loadings, 
                               mc_n = 5, 
                               plot = TRUE)