This function takes a definition of weight transformation limits and corresponding minimum and maximum numbers of end-members to model all end-member scenarios in accordance with these parameters. Based on the output the user can decide on robust end-members.
model.em(X, q, l, plot = TRUE, col.q = TRUE, bw, ...)Numeric matrix with m samples (rows) and n variables (columns).
Numeric matrix, definitions of minimum and maximum number of
end-members (cf. get.q()), required.
Numeric vector, weight transformation limit values, corresponding to the matrix q, required.
Logical scalar, option to plot the results (cf. details for
explanations), default is TRUE.
Logical scalar, option to colour end-member loadings by the
number of end-members which were used to create the model realisation,
default is TRUE.
Numeric scalar, optional manual setting of the kde bandwidth. By default, bw is calculated as 1 percent of the number of grain-size classes.
Further arguments passed to the function.
List object with all modelled end-members, each described by
input parameters, mode position, quality measures and value distributions.
The plot output is an overlay of several data. The coloured lines in the
background are end-member loadings (number noted in the plot title),
resulting from all possible model scenarios. If col.q == TRUE they
are coloured according to the number of end-members with which the model
was generated. This colour scheme allows to depict end-members that emerge
for model realisations with specific number of end-members. The thick
black line is a kernel density estimate curve, generated from the mode
positions of all end-members. The kernel bandwidth is set to 1 percent of
the number of grain-size classes of the input data set, which gave useful
results for most of our test data sets. The cumulaitve dot-line-plot is a
further visualisation of end-member mode positions. The function is a
modified wrapper function for the function test.robustness().
Dietze E, Hartmann K, Diekmann B, IJmker J, Lehmkuhl F, Opitz S, Stauch G, Wuennemann B, Borchers A. 2012. An end-member algorithm for deciphering modern detrital processes from lake sediments of Lake Donggi Cona, NE Tibetan Plateau, China. Sedimentary Geology 243-244: 169-180.
# NOT RUN {
## load example data set
data(X, envir = environment())
## define input parameters
l <- c(0, 0.05, 0.10)
q <- cbind(c(2, 2, 3), c(5, 6, 4))
## infer l-vector
em_pot <- model.em(X = X, q = q, l = l)
# }
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