```
# remove previously loaded items from the current environment and remove previous graphics.
rm(list=ls())
graphics.off()
# Here, I set the seed each time so that the results are comparable.
# This is useful as it means that anyone that runs your code, *should*
# get the same results as you, although random number generators change
# from time to time.
set.seed(1)
# load SIBER
library(SIBER)
library(viridis)
# set a new three-colour palette from the viridis package
palette(viridis::viridis(3))
# load in the included demonstration dataset
data("demo.siber.data")
#
# create the siber object
siber.example <- createSiberObject(demo.siber.data)
# Or if working with your own data read in from a *.csv file, you would use
# This *.csv file is included with this package. To find its location
# type
# fname <- system.file("extdata", "demo.siber.data.csv", package = "SIBER")
# in your command window. You could load it directly by using the
# returned path, or perhaps better, you could navigate to this folder
# and copy this file to a folder of your own choice, and create a
# script from this vingette to analyse it. This *.csv file provides
# a template for how your own files should be formatted.
# mydata <- read.csv(fname, header=T)
# siber.example <- createSiberObject(mydata)
# Create lists of plotting arguments to be passed onwards to the
# plotting functions. With p.interval = NULL, these are SEA. NB not SEAc though
# which is what we will base our overlap calculations on. This implementation
# needs to be added in a future update. For now, the best way to plot SEAc is to
# add the ellipses manually following the vignette on this topic.
group.ellipses.args <- list(n = 100, p.interval = NULL, lty = 1, lwd = 2)
par(mfrow=c(1,1))
plotSiberObject(siber.example,
ax.pad = 2,
hulls = F, community.hulls.args,
ellipses = T, group.ellipses.args,
group.hulls = F, group.hull.args,
bty = "L",
iso.order = c(1,2),
xlab = expression({delta}^13*C~'permille'),
ylab = expression({delta}^15*N~'permille')
)
```

In order now to calculate the overlap between two (or more) ellipses,
we need to know the coordinates of each ellipse. This is done by calling
`addEllipse(..., do.plot = FALSE)`

. See the associated help
file and the vignette Customising-Plots-Manually
for more information on optional inputs to addEllipse for different
types of ellipse. Also, bear in mind that the option `n`

controls how many points are used to draw the ellipse, and hence low
`n`

means clunky, edgier ellipses, compared with rounder,
smoother ellipses for higher `n`

. A higher `n`

is
more suitable when ellipses are more eccentric as their curvature is
greater at the tips. The new functions `maxLikOverlap`

and
`bayesianOverlap`

are wrapper functions that take care of the
calls to `addEllipse`

and the actual polygon overlap function
in the package `spatstat.utils`

. The functions
`maxLikOverlap`

and `bayesianOverlap`

return three
values each: the computationally calculated area of the first ellipse,
second ellipse, and the overlap between them. It is not entirely obvious
to me that there is a single choice if you wish to express your overlap
as a proportion, since there are several options for the choice of
denominator. One can imagine that expressing the overlap as a proportion
of the sum of the non-overlapping areas of the ellipses seems suitable
in a general sense, since this will range from 0 when the ellipses are
completely distinct, to 1 when the ellipses are completely
coincidental.

```
# In this example, I will calculate the overlap between ellipses for groups 2
# and 3 in community 1 (i.e. the green and yellow open circles of data).
# The first ellipse is referenced using a character string representation where
# in "x.y", "x" is the community, and "y" is the group within that community.
# So in this example: community 1, group 2
ellipse1 <- "1.2"
# Ellipse two is similarly defined: community 1, group3
ellipse2 <- "1.3"
# The overlap of the maximum likelihood fitted standard ellipses are
# estimated using
sea.overlap <- maxLikOverlap(ellipse1, ellipse2, siber.example,
p.interval = NULL, n = 100)
# the overlap betweeen the corresponding 95% prediction ellipses is given by:
ellipse95.overlap <- maxLikOverlap(ellipse1, ellipse2, siber.example,
p.interval = 0.95, n = 100)
# so in this case, the overlap as a proportion of the non-overlapping area of
# the two ellipses, would be
prop.95.over <- ellipse95.overlap[3] / (ellipse95.overlap[2] +
ellipse95.overlap[1] -
ellipse95.overlap[3])
```

The function `bayesianOverlap`

returns multiple rows of
these three numbers, each representing the values for a particular draw
from the posterior estimates so that you can build up a picture of the
distribution of the estimated overlap. Calculating this overlap is
computationally time consuming, and there are going to be thousands of
posterior samples collected in a typical analysis. For this example, I
will calculate the posterior overlap on the first 100 samples, but in
reality you would probably want to do this on at least a few hundred, if
not all your posterior samples in a longer (perhaps over-lunch or
over-night) run.

```
# options for running jags
parms <- list()
parms$n.iter <- 2 * 10^4 # number of iterations to run the model for
parms$n.burnin <- 1 * 10^3 # discard the first set of values
parms$n.thin <- 10 # thin the posterior by this many
parms$n.chains <- 2 # run this many chains
# define the priors
priors <- list()
priors$R <- 1 * diag(2)
priors$k <- 2
priors$tau.mu <- 1.0E-3
# fit the ellipses which uses an Inverse Wishart prior
# on the covariance matrix Sigma, and a vague normal prior on the
# means. Fitting is via the JAGS method.
ellipses.posterior <- siberMVN(siber.example, parms, priors)
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 30
#> Unobserved stochastic nodes: 3
#> Total graph size: 45
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 30
#> Unobserved stochastic nodes: 3
#> Total graph size: 45
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 30
#> Unobserved stochastic nodes: 3
#> Total graph size: 45
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 30
#> Unobserved stochastic nodes: 3
#> Total graph size: 45
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 30
#> Unobserved stochastic nodes: 3
#> Total graph size: 45
#>
#> Initializing model
#>
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 30
#> Unobserved stochastic nodes: 3
#> Total graph size: 45
#>
#> Initializing model
# and teh corresponding Bayesian estimates for the overlap between the
# 95% ellipses is given by:
bayes95.overlap <- bayesianOverlap(ellipse1, ellipse2, ellipses.posterior,
draws = 100, p.interval = 0.95, n = 100)
# a histogram of the overlap
hist(bayes95.overlap[,3], 10)
```