minDC: Extract minimum dissimilarities

In analogue: Analogue and Weighted Averaging Methods for Palaeoecology

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), ...)

## S3 method for class '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), ...)

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.

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

## 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.)")

analogue documentation built on Sept. 30, 2024, 9:41 a.m.