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 ImbrieKipp example from ?join example(join) ## 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, k = 10) ## 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.)")