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 Gaussiandistributed 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
D.S. Wilks (1998), Multisite Generalization of a Daily Stochastic Precipitation Generation Model, Journal of Hydrology, Volume 210, Issues 14, September 1998, Pages 178191, http://www.sciencedirect.com/science/article/pii/S0022169498001863
Muamaraldin Mhanna and Willy Bauwens (2011) A Stochastic SpaceTime Model for the Generation of Daily Rainfall in the Gaza Strip, International Journal of Climatology, Volume 32, Issue 7, pages 10981112, http://dx.doi.org/10.1002/joc.2305
normalCopula
,pcopula
1 2 3 
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