analogue (version 0.4-0)

minDC: Functions to 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, ...)
## S3 method for class 'default':
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.

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.)")