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Last data update: 2014.03.03

R: Function to evaluate influence of model parameters.

test.parameters

R Documentation

Function to evaluate influence of model parameters.

Description

All possible combinations of number of end-members and weight transformation limits are used to perform EMMA. The function returns matrices of absolute and relative measures of individual model performance.

Usage

test.parameters(X, q, l = 0, c = 100, rotation = "Varimax",
  plot = FALSE, legend, progressbar = FALSE, multicore = FALSE, ...,
  pm = FALSE)

Arguments

`X`	Numeric matrix with m samples (rows) and n variables (columns).
`q`	Numeric vector of length two, specifying the minimum and maximum number of end-members to be modelled.
`l`	Numeric vector specifying the weight tranformation limit, i.e. quantile; default is 0.
`c`	Numeric scalar specifying the constant sum scaling parameter, e.g. 1, 100, 1000; default is 0.
`rotation`	Character scalar, rotation type, default is "Varimax" (cf. Dietze et al., 2012). One out of the rotations provided in GPArotation is possible (cf. `rotations`).
`plot`	Character scalar, optional graphical output of the results. Specify which tested parameter will be plotted: "mEm" (mean absolute row-wise error), "mEn" (mean absolute column-wise error), "mRm" (mean relative row-wise error), "mRn" (mean relative column-wise error), "mRt" (mean relative total error), "ol" (number of overlapping end-members). All plots except "ol" are colour-coded bitmaps of q, l and the specified test parameter and line-plots the specified parameter vs. q.
`legend`	Character scalar, specifying legend position (cf. `legend`). If omitted, no legend will be plotted, default is no legend.
`progressbar`	Logical scalar, optionally show a progress bar, default is `FALSE`. Only available if option `multicore` is not used.
`multicore`	Logical scalar, optionally ditribute calculations to all available cores of the computer, default is `TRUE`.
`pm`	Logical scalar to enable pm.
`...`	Additional arguments passed to the plot function. Since the function returns two plots (except for plot option "ol"), additional graphical parameters must be specified as vector with the first element for the first plot and the second element for the second plot. If graphical parameters are natively vectors (e.g. a sequence of colours), they must be specified as matrices with each vector as a row. A legend can only be added to the second plot. Colours only apply to the second plot as well. If colours are specified, `colour` should be used instead of `col`. See example section for further advice.

Details

The mean total explained variance mRt may be used to define a maximum number of meaningful end-members for subsequent modelling, e.g. as the number of end-members, which reaches the first local mRt maximum.

Overlapping is defined as one end-member having its mode within the "area" of any other end-member, which is genetically not explainable.

Special acknowledgements go to Christoph Burow for his efforts to implement the multicore functionality to this function.

Value

A list with result objects

`mEm`	Absolute row-wise model error.
`mEn`	Absolute column-wise model error.
`mRm`	Mean row-wise explained variance.
`mRn`	Mean column-wise explained variance.
`mRt`	Mean total explained variance.
`ol`	Number of overlapping end-member loadings.
`q.max`	Maximum number of meaningful end-members.

Author(s)

Michael Dietze, Elisabeth Dietze

References

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.

Examples

## load example data set
data(X, envir = environment())

## truncate the data set for faster computation
X.trunc <- X[1:20,]

## define test parameters
q <- 2:8 # number of end-members
l <- seq(from = 0, to = 0.3, by = 0.1)

## test parameter influence and plot mean total explained variance
TP <- test.parameters(X = X.trunc, q = q, l = l, plot = "mRt",
                      legend = "bottomright", cex = 0.7,
                      multicore = FALSE,
                      colour = rgb((1:7) / 7, 0.9, 0.2, 1))

## show maximum number of end-members
TP$q.max

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(EMMAgeo)
Loading required package: GPArotation
Loading required package: limSolve
Loading required package: shape
Loading required package: shiny
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/EMMAgeo/test.parameters.Rd_%03d_medium.png", width=480, height=480)
> ### Name: test.parameters
> ### Title: Function to evaluate influence of model parameters.
> ### Aliases: test.parameters
> ### Keywords: EMMA
> 
> ### ** Examples
> 
> ## load example data set
> data(X, envir = environment())
> 
> ## truncate the data set for faster computation
> X.trunc <- X[1:20,]
> 
> ## define test parameters
> q <- 2:8 # number of end-members
> l <- seq(from = 0, to = 0.3, by = 0.1)
> 
> ## test parameter influence and plot mean total explained variance
> TP <- test.parameters(X = X.trunc, q = q, l = l, plot = "mRt",
+                       legend = "bottomright", cex = 0.7,
+                       multicore = FALSE,
+                       colour = rgb((1:7) / 7, 0.9, 0.2, 1))
> 
> ## show maximum number of end-members
> TP$q.max
[1] 5 4 5 4
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>