fit.GWex.prec | R Documentation |
estimate all the parameters for the G-Wex model of precipitation
fit.GWex.prec(objGwexObs, parMargin, listOption = NULL)
objGwexObs |
object of class |
parMargin |
if not NULL, list where each element parMargin[[iM]] corresponds to a month iM=1...12 and contains a matrix nStation x 3 of estimated parameters of the marginal distributions (EGPD or mixture of exponentials) |
listOption |
list with the following fields:
|
a list containing the list of options listOption
and the list of estimated parameters listPar
.
The parameters of the occurrence process are contained in parOcc
and the parameters related to the precipitation
amounts are contained in parInt
. Each type of parameter is a list containing the estimates for each month. In parOcc
, we find:
p01: For each station, the probability of transition from a dry state to a wet state.
p11: For each station, the probability of staying in a wet state.
list.pr.state: For each station, the probabilities of transitions for a Markov chain with lag p
.
list.mat.omega: The spatial correlation matrix of occurrences \Omega
(see Evin et al., 2018).
In parInt
, we have:
parMargin: list of matrices nStation x nPar of parameters for the marginal distributions (one element per Class).
cor.int: Matrices nStation x nStation M_0
, A
, \Omega_Z
representing
the spatial and temporal correlations between all the stations (see Evin et al., 2018). For the
Student copula, dfStudent
indicates the \nu
parameter.
Guillaume Evin
Evin, G., A.-C. Favre, and B. Hingray. 2018. 'Stochastic Generation of Multi-Site Daily Precipitation Focusing on Extreme Events.' Hydrol. Earth Syst. Sci. 22 (1): 655-672. doi.org/10.5194/hess-22-655-2018.
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