mi_cyl {cylcop} | R Documentation |
Estimate the Mutual Information Between a Circular and a Linear Random Variable
Description
The empirical copula is obtained from the data (theta
and x
),
and the mutual information of the 2 components is calculated. This gives a
non-negative number that can be normalized to lie between 0 and 1.
Usage
mi_cyl(theta, x, normalize = TRUE, symmetrize = FALSE)
Arguments
theta |
numeric vector of angles (measurements of a circular variable). |
x |
numeric vector of step lengths (measurements of a linear variable). |
normalize |
logical value whether the mutual information should be
normalized to lie within |
symmetrize |
logical value whether it should be assumed that right and left
turns are equivalent. If |
Details
First, the two components of the empirical copula, u
and v
are obtained. Then the mutual information is calculated via discretizing u
and v
into length(theta)^(1/3)
bins. The mutual information can be
normalized to lie between 0 and 1 by dividing by the product of the entropies
of u
and v
. This is done using functions from the 'infotheo'
package.
Even if u
and v
are perfectly correlated
(i.e. cor_cyl
goes to 1 with large sample sizes),
the normalized mutual information will not be 1 if the underlying copula is periodic and
symmetric. E.g. while normalCopula(1)
has a correlation of 1 and a density
that looks like a line going from (0,0)
to (1,1)
,
cyl_rect_combine(normalCopula(1))
has a density that looks like "<". The mutual information will be 1 in the first case,
but not in the second. Therefore, we can set symmetrize = TRUE
to first
convert (if necessary) theta to lie in [-\pi, \pi)
and then multiply all angles
larger than 0 with -1. The empirical copula is then calculated and the mutual information
is obtained from those values. It is exactly 1 in the case of
perfect correlation as captured by e.g.
cyl_rect_combine(normalCopula(1))
.
Note also that the mutual information is independent of the marginal distributions.
However, symmetrize=TRUE
only works with angles, not with pseudo-observations.
When x
and theta
are pseudo-observations, information is lost
due to the ranking, and symmetrization will fail.
Value
A numeric value, the mutual information between theta
and x
in nats.
References
Ma J, Sun Z (2011). “Mutual Information Is Copula Entropy.” Tsinghua Science and Technology, 16(1), 51-54. ISSN 1007-0214, doi:10.1016/S1007-0214(11)70008-6, https://www.sciencedirect.com/science/article/pii/S1007021411700086/.
Calsaverini RS, Vicente R, Systems C, Artes ED (2009). “An information-theoretic approach to statistical dependence: Copula information.” Europhysics Letters, 88(6), 1–6. doi:10.1209/0295-5075/88/68003, https://iopscience.iop.org/article/10.1209/0295-5075/88/68003/.
Hodel FH, Fieberg JR (2021). “Cylcop: An R Package for Circular-Linear Copulae with Angular Symmetry.” bioRxiv. doi:10.1101/2021.07.14.452253, https://www.biorxiv.org/content/10.1101/2021.07.14.452253v3/.
See Also
cor_cyl()
, fit_cylcop_cor()
.
Examples
set.seed(123)
cop <- cyl_quadsec(0.1)
marg1 <- list(name="vonmises",coef=list(0,4))
marg2 <- list(name="lnorm",coef=list(2,3))
#draw samples and calculate the mutual information.
sample <- rjoint(100,cop,marg1,marg2)
mi_cyl(theta = sample[,1],
x = sample[,2],
normalize = TRUE,
symmetrize = FALSE
)
#the correlation coefficient is independent of the marginal distribution.
sample <- traj_sim(100,
cop,
marginal_circ = list(name = "vonmises", coef = list(0, 1)),
marginal_lin = list(name = "weibull", coef = list(shape = 2))
)
mi_cyl(theta = sample$angle,
x = sample$steplength,
normalize = TRUE,
symmetrize = FALSE)
mi_cyl(theta = sample$cop_u,
x = sample$cop_v,
normalize = TRUE,
symmetrize = FALSE)
# Estimate correlation of samples drawn from circular-linear copulas
# with perfect correlation.
cop <- cyl_rect_combine(copula::normalCopula(1))
sample <- rjoint(100,cop,marg1,marg2)
# without normalization
mi_cyl(theta = sample[,1],
x = sample[,2],
normalize = FALSE,
symmetrize = FALSE
)
#with normalization
mi_cyl(theta = sample[,1],
x = sample[,2],
normalize = TRUE,
symmetrize = FALSE
)
#only with normalization and symmetrization do we get a value of 1
mi_cyl(theta = sample[,1],
x = sample[,2],
normalize = TRUE,
symmetrize = TRUE
)