model.EM: Model all possible end-member scenarios
In coffeemuggler/EMMAgeo: End-Member Modelling of Grain-Size Data

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1	model.EM(X, q, l, classunits, plot = TRUE, col.q = TRUE, bw, ...)

`X`	`Numeric` matrix, input data set with m samples (rows) and n variables (columns).
`q`	`Numeric` matrix, definitions of minimum and maximum number of end-members (cf. `get.q()`), required.
`l`	`Numeric` vector, weight transformation limit values, corresponding to the matrix q, required.
`classunits`	`Numeric` vector, optional class units (e.g. micrometers or phi-units) of the same length as columns of `X`.
`plot`	`Logical` scalar, option to plot the results (cf. details for explanations), default is `TRUE`.
`col.q`	`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`.
`bw`	`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.

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().

List object with all modelled end-members, each described by input parameters, mode position, quality measures and value distributions.

Michael Dietze, Elisabeth Dietze

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.

EMMA, test.l.max

## load example data set
data(example_X)

## 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)