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), ...)

## 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

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

### Examples

```## Imbrie and Kipp example
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[] / 100
V12.122 <- dat[] / 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.)")
```

[Package analogue version 0.17-6 Index]