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), ...)
x |
an object. Currently only "default" and
"predict.mat" methods available. |
probs |
numeric; vector of probabilities with values in [0,1]. |
... |
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.mat
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.)")