Extracts and formats close modern analogue samples from a modern reference set that are closer than a defined cut off threshold.
cma(object, ...)# S3 method for default
cma(object, ...)
# S3 method for analog
cma(object, cutoff, prob = c(0.01, 0.025, 0.05), ...)
# S3 method for mat
cma(object, k, cutoff, prob = c(0.01, 0.025, 0.05), ...)
# S3 method for predict.mat
cma(object, k, cutoff, prob = c(0.01, 0.025,
0.05), ...)
# S3 method for cma
plot(x, method = c("overplot", "jitter", "stack"),
jitter = 0.1, vertical = FALSE,
draw.quant = TRUE, xlab = NULL, ylab = "",
main = "", cex.axis = NULL, ...,
col.quant = "red", lty.quant= "dashed")
For the plot method, a plot on the current device. Invisibly the plotted data are returned; see Note for further details.
A list of class "cma" with the following components:
a named list of named vectors of close modern analogues
and their dissimilarities. The names of the list components are the
names of the fossil samples. The named vector in each
component of close is the distances for the close modern
analogues from the training set that are as close as cutoff,
or closer, to the fossil sample.
the matched call.
the cutoff threshold used to define close modern analogues.
numeric vector of the requested quantiles. Note returned
by the predict.mat method.
the probabilities of the requested quantiles.
character; the dissimilarity coefficient used
numeric vector of the number of analogues per fossil sample.
an object for which close modern analogues are to be
returned. Currently only for objects of class analog.
numeric; the number of analogues to return.
numeric; critical value determining level below which
samples from the modern reference set are defined as close modern
analogues. May be missing, in which case the 2.5% quantile of the
training set dissimilarities is used unless object$train is
NULL, in which case "cutoff" must be supplied.
numeric vector of probabilities with values in [0,1], for
which quantiles of the distribution of training set dissimilarities
will be calculated. See quantile.
arguments to be passed to other cma
methods or additional arguments passed to stripchart.
an object of class "cma".
the method to be used to separate coincident points. The
default method"overplot" causes such points to be
overplotted, but it is also possible to specify "jitter" to
jitter the points, or "stack" have coincident points
stacked. The last method only makes sense for very granular data.
when method="jitter" is used, jitter gives
the amount of jittering applied.
when vertical is TRUE the plots are drawn
vertically rather than the default horizontal.
logical; should the quantiles be drawn on the stripchart?
Graphical parameters
The magnification to be used for axis annotation
relative to the current setting of cex. See
par.
colour and line type in which to drawn the quantile lines.
Gavin L. Simpson
The plot method is simply a wrapper to stripchart.
The methods for mat and predict.mat objects allow the
user to select the k-closest analogues (argument k) or those
samples as close or closer than a stated threshold of dissimilarity
(argument cutoff). Only one of k and cutoff may
be specified. If neither is specified, getK is used to
extract the value for k stored within object. As such,
the default is to return the automatically selected set of k
closest samples, behaviour that is consistent with other functions in
the package.
Flower, R.J., Juggins, S. and Battarbee, R.W. (1997) Matching diatom assemblages in lake sediment cores and modern surface sediment samples: the implications for lake conservation and restoration with special reference to acidified systems. Hydrobiologia 344; 27--40.
Simpson, G.L., Shilland, E.M., Winterbottom, J. M. and Keay, J. (2005) Defining reference conditions for acidified waters using a modern analogue approach. Environmental Pollution 137; 119--133.
analog, stripchart, or
boxplot for an alternative representation.
## 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
## analog matching between SWAP and RLGH reference samples
(ik.ana <- analog(ImbrieKipp, V12.122, method = "chord"))
## close modern analogues
(ik.cma <- cma(ik.ana, cutoff = 0.4))
summary(ik.cma)
## plot the results
plot(ik.cma)
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