minDC {analogue}R Documentation

Extract minimum dissimilarities

Description

Minimum dissimilarity is a useful indicator of reliability of reconstructions performed via MAT and other methods, and for analogue matching. Minimum dissimilarity for a sample is the smallest dissimilarity between it and the training set samples.

Usage

minDC(x, ...)

## Default S3 method:
minDC(x, ...)

## S3 method for class 'predict.mat':
minDC(x, ...)

## S3 method for class 'analog':
minDC(x, probs = c(0.01, 0.02, 0.05, 0.1), ...)

Arguments

x an object of class "predict.mat", "analog" or a object with a component named "minDC".
probs numeric; vector of probabilities with values in [0,1].
... other arguments to be passed to other methods. Currently ignored.

Value

minDC returns an object of class "minDC".
An object of class minDC is a list with some or all of the following components:

minDC numeric; vector of minimum dissimilarities.
method character; the dissimilarity coefficient used.
quantiles numeric; named vector of probability quantiles for distribution of dissimilarities of modern training set.

Note

The "default" method of minDC will attempt to extract the relevant component of the object in question. This may be useful until a specific minDC method is written for a given class.

Author(s)

Gavin L. Simpson

See Also

predict.mat, and plot.minDC for a plotting method.

Examples

## continue the RLGH example from ?join
example(join)

## fit the MAT model using the squared chord distance measure
swap.mat <- mat(swapdiat, swappH, method = "SQchord")
swap.mat

## reconstruct for the RLGH core data
rlgh.mat <- predict(swap.mat, rlgh, k = 10)

## extract the minimum DC values
rlgh.mdc <- minDC(rlgh.mat)
rlgh.mdc

## draw a plot of minimum DC by time
plot(rlgh.mdc, use.labels = TRUE, xlab = "Depth (cm.)")

[Package analogue version 0.4-3 Index]