This function adds an ellipse based on means and covariance to an existing plot. The ellipse can be scaled so as to represent any prediction interval of the data you wish, or alternatively any confidence interval of the bivariate means.
1 2 
mu 
a vector of length two specifying the bivariate means 
sigma 
a 2x2 covariance matrix for the data 
m 
is the sample size of the dataset on which the ellipse is to be
plotted. This is only informative if calculating the confidence interval of
the bivariate mean, which requires a correction of 
n 
the number of data points to be used to plot the ellipse. More
points makes for a smoother ellipse, especially if it has high
eccentricity. Defaults to 
p.interval 
the quantile to be used to construct a prediction ellipse
that contains p.interval proportion of the data. By default,

ci.mean 
a logical that determines whether the ellipse drawn is a
prediction ellipse of the entire data, or a confidence interval of the
bivariate means. Defaults to 
small.sample 
a logical that determines whether or not the small sample size correction is to be applied (TRUE) or not (FALSE). Defaults to FALSE. This allows SEAc rather than SEA to be plotted, but the correction can be applied to any percentile ellipse. 
do.plot 
A logical that determines whether plotting should occur (TRUE and default) or not (FALSE). Setting to false is useful if you want to access the coordinates of the ellipse in order to calculate overlap between ellipses for example. 
... 
additional arguments as a list to be passed to

A n x 2
matrix comprising the x and y coordinates of the
ellipse.
1 2 3  data(demo.siber.data)
my.siber.data < createSiberObject(demo.siber.data)
communityMetricsML(my.siber.data)

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