Description Usage Arguments Value Note Author(s) See Also Examples
This function finds the bivariate joint probability or the
binary correlation from the corresponding Gaussian
correlation x
1 |
x |
value of expected correlation between the corresponding Gaussian-distributed variables |
p0_v1,p0_v2 |
probability of no precipitation
occurences for the v1 and v2 time series respectively.
See |
correlation |
logical numeric value. Default is
|
probability of no precipitation occurence in both v1 and v2
simultaneously. It is a matrix if x
is a matrix.
This function makes use of normal copula. A graphical
introduction to this function (with its inverse) makes is
present in the following URL references:
http://onlinelibrary.wiley.com/doi/10.1002/joc.2305/abstract
and
http://www.sciencedirect.com/science/article/pii/S0022169498001863
(See fig. 1 and par. 3.2) If the argument p0_v2
, the
two marginal probabily values must be given as a vector
through the argument p0_v1
:
p0_v1=c(p0_v1,p0_v2)
. In case x
is a
correlation/covariance matrix the marginal probabilities
are given as a vector through the argument p0_v1
.
Emanuele Cordano
1 2 3 |
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