EMMAgeo (version 0.9.8)

robust.EM: Extract robust end-members

Description

This function takes a list object with potential end-member loadings and extracts those with modes in specified limits to describe them by mean and standard deviation and use these descriptions to propagate the uncertainties to end-member scores.

Usage

robust.EM(
  em,
  limits,
  classunits,
  amount,
  l,
  mc_n,
  type = "mean",
  qt = c(0.25, 0.75),
  cores = 1,
  plot = FALSE,
  ...
)

Value

List with statistic descriptions of end-member loadings and scores.

Arguments

em

List of class "EMMAgeo_empot", i.e. the outout of model.em() or test.robustness(), containing potential end-members, both in unscaled and rescaled version as well as further parameters.

limits

Numeric matrix with two columns, defining the class limits for the robust end-members to calculate. The first column defines the lower limits, the second column the upper limits. End-members are organised in rows.

classunits

Numeric vector, optional class units (e.g. micrometers or phi-units) of the same length as columns of X.

amount

Numeric matrix with two columns, defining the minimum and maximum amount of the modal class for each end-member.

l

Numeric scalar, weight transformation limit for modelling the average end-member output.

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.

type

Character scalar, type of oadings statistics. One out of "mean" and "median". Default is "mean".

qt

Numeric vector of length two, quantiles to describe end-member loadings. Default is c(0.25, 0.75) (i.e., the quartile range).

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

Details

The function is used to extract potential end-member loadings based on their mode positions and, optionally the height of the mode class, and use them to infer mean and stanard deviation of all end-members that match the group criteria defined by limits. These information are then used to model the uncertainty of the corresponding end-member scores. The function uses input from two preceeding approaches. In a compact protocol model.em delivers these data in a predefined way. In the extended protocol test.robustness does this.

See Also

robust.loadings, robust.scores

Examples

if (FALSE) {

## load example data set
data(example_X)

## get weight transformation limit vector
l <- get.l(X = X)

## get minimum and maximum number of end-members
q <- get.q(X = X, l = l)

## get all potential model scenarios
EM_pot <- model.EM(X = X, q = q, plot = TRUE)

## define end-member mode class limits
limits <- cbind(c(61, 74, 95, 102), 
                c(64, 76, 100, 105))

## get robust end-members in the default way, with plot output
rem <- robust.EM(em = EM_pot,
                 limits = limits,
                 plot = TRUE)
                    
## get robust end-members by only modelling uncertainty in loadings
robust_EM <- robust.EM(em = EM_pot, 
                       limits = limits, 
                       plot = TRUE)

}