This function finds the bivariate joint probability or the binary correlation from the corresponding Gaussian correlation x
This function finds the bivariate joint probability or the
binary correlation from the corresponding Gaussian
value of expected correlation between the corresponding Gaussian-distributed variables
probability of no precipitation
occurences for the v1 and v2 time series respectively.
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:
(See fig. 1 and par. 3.2) If the argument
two marginal probabily values must be given as a vector
through the argument
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
D.S. Wilks (1998), Multisite Generalization of a Daily Stochastic Precipitation Generation Model, Journal of Hydrology, Volume 210, Issues 1-4, September 1998, Pages 178-191, http://www.sciencedirect.com/science/article/pii/S0022169498001863
Muamaraldin Mhanna and Willy Bauwens (2011) A Stochastic Space-Time Model for the Generation of Daily Rainfall in the Gaza Strip, International Journal of Climatology, Volume 32, Issue 7, pages 1098-1112, http://dx.doi.org/10.1002/joc.2305
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