minDC: 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 default
minDC(x, ...)
# S3 method for predict.mat
minDC(x, ...)
# S3 method for analog
minDC(x, probs = c(0.01, 0.02, 0.05, 0.1), ...)
# S3 method for wa
minDC(x, y,
      method = c("euclidean", "SQeuclidean", "chord", "SQchord",
                 "bray", "chi.square", "SQchi.square", "information",
                 "chi.distance", "manhattan", "kendall", "gower",
                 "alt.gower", "mixed"),
      percent = FALSE, probs = c(0.01, 0.025, 0.05, 0.1), ...)

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.

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].
y: an optional matrix-like object containing fossil samples for which the minimum dissimilarities to training samples are to be calculated.
method: character; which choice of dissimilarity coefficient to use. One of the listed options. See distance.
percent: logical; Are the data percentages? If TRUE, the data (x and y) will be divided by 100 to convert them to the proportions expected by distance.
...: other arguments to be passed to other methods. Currently ignored.

Author

Gavin L. Simpson

Examples

Run this code

## Imbrie and Kipp example
## load the example data
data(ImbrieKipp)
data(SumSST)
data(V12.122)

## merge training and test set on columns
dat <- join(ImbrieKipp, V12.122, verbose = TRUE)

## extract the merged data sets and convert to proportions
ImbrieKipp <- dat[[1]] / 100
V12.122 <- dat[[2]] / 100

## fit the MAT model using the squared chord distance measure
ik.mat <- mat(ImbrieKipp, SumSST, method = "SQchord")
ik.mat

## reconstruct for the V12-122 core data
v12.mat <- predict(ik.mat, V12.122)

## extract the minimum DC values
v12.mdc <- minDC(v12.mat)
v12.mdc

## draw a plot of minimum DC by time
plot(v12.mdc, use.labels = TRUE, xlab = "Depth (cm.)")

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