| 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 "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. |
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.
## continue the RLGH example from ?join example(join) ## fit the MAT model using the squared chord distance measure swap.mat <- mat(swapdiat, swappH, method = "SQchord") swap.mat ## reconstruct for the RLGH core data rlgh.mat <- predict(swap.mat, rlgh, k = 10) ## extract the minimum DC values rlgh.mdc <- minDC(rlgh.mat) rlgh.mdc ## draw a plot of minimum DC by time plot(rlgh.mdc, use.labels = TRUE, xlab = "Depth (cm.)")