minDC {analogue} | R Documentation |

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.

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), ...)

`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. |

`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. |

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.

Gavin L. Simpson

`predict.mat`

, and `plot.minDC`

for a
plotting method.

## 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.)")

[Package *analogue* version 0.17-6 Index]