Description Usage Arguments Value Note Author(s) References 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 occurrences for the v1 and v2 time series respectively. See |
correlation |
logical numeric value. Default is |
probability of no precipitation occurrence 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: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.2305
and https://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 1-4, September 1998, Pages 178-191, https://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, doi: 10.1002/joc.2305, https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.2305
1 2 3 |
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