plot.dissimilarities: Plots the distribution of extracted dissimilarities

In analogue: Analogue and Weighted Averaging Methods for Palaeoecology

Plots the distribution of extracted dissimilarities

Description

Produces a plot of the distribution of the extracted dissimilarities and a reference normal distribution with comparable mean and sd.

Usage

## S3 method for class 'dissimilarities'
plot(x, prob = 0.05,
     legend = TRUE, n.rnorm = 1e+05, col = "black",
     col.ref = "red", lty = "solid", lty.quant = "dotted",
     xlab = NULL, ylab = NULL, main = NULL, sub = NULL, ...)

Arguments

x	an object of class "dissimilarities".
prob	numeric; density probability defining the threshold for close modern analogues.
legend	logical; draw a legend on the plotted figure?
n.rnorm	numeric; number of random normal deviates for reference line.
col, col.ref	colours for the dissimilarity and reference density functions drawn.
lty, lty.quant	line types for the dissimilarity and reference density functions drawn.
xlab, ylab	character; x- and y-axis labels.
main, sub	character; main and subtitle for the plot.
...	graphical arguments passed to other graphics functions.

Value

A plot on the currently active device.

Author(s)

Gavin L. Simpson

See Also

dissimilarities

Examples

## 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 SWAPImbrie & Kipp and V12.122 core
ik.analog <- analog(ImbrieKipp, V12.122, method = "chord")
ik.analog
summary(ik.analog)

## compare training set dissimilarities with normals
## and derive cut-offs
ik.dij <- dissim(ik.analog)
plot(ik.dij)

analogue documentation built on Sept. 30, 2024, 9:41 a.m.