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 |

`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 |

`percent` |
logical; Are the data percentages? If |

`...` |
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*version 0.17-6 Index]