GWEX (version 1.0.2)

fitGwexModel: fitGwexModel: fit a GWex model to observations.

Description

fitGwexModel: fit a GWex model to observations.

Usage

fitGwexModel(objGwexObs, listOption = NULL)

Arguments

objGwexObs

an object of class '>GwexObs

listOption

for precipitation, a list with the following fields:

• th: threshold value in mm above which precipitation observations are considered to be non-zero (=0.2 by default)

• nLag: order of the Markov chain for the transitions between dry and wet states (=2 by default)

• typeMargin: 'EGPD' (Extended GPD) or 'mixExp' (Mixture of Exponentials). 'EGPD' by default

• xiHat: pre-determined values for the xi parameters of the EGPD distribution on precipitation amounts

• copulaInt: 'Gaussian' or 'Student': type of dependence for amounts (='Student' by default)

• isMAR: logical value, do we apply a Autoregressive Multivariate Autoregressive model (order 1) =TRUE by default

• is3Damount: logical value, do we apply the model on 3D-amount. =FALSE by default

• nChainFit: integer, length of the runs which are generated during the fitting procedure. =100000 by default

• nCluster: integer, number of clusters which can be used for the parallel computation

and for temperature, a list with the following fields:

• hasTrend: logical value, do we fit a linear trend for the long-term change, =FALSE by default

• objGwexPrec: object of class '>GwexObs containing precipitation observations. If provided, we assume that temperature must be modelled and simulated according to the precipitation states 'dry' and 'wet'. For each state, a seasonal cycle is fitted (mean and sd).

• typeMargin: 'SGED' (default) or 'Gaussian': type of marginal distribution.

• depStation: 'MAR1' (default) or 'Gaussian': MAR1 (Multivariate Autoregressive model order 1) for the spatial and temporal dependence or 'Gaussian' for the spatial dependence only.

Value

Return an object of class '>GwexFit with:

• p: The number of station,

• version: package version,

• variable: the type of variable,

• fit: a list containing the list of options listOption and the list of estimated parameters listPar.

Examples

# NOT RUN {
# Format dates corresponding to daily observations of precipitation and temperature
vecDates = seq(from=as.Date("01/01/2005",format="%d/%m/%Y"),
to=as.Date("31/12/2014",format="%d/%m/%Y"),by='day')

###############################################################
#               FIT THE PRECIPITATION MODEL
###############################################################

# Format observations: create a G-Wex object
myObsPrec = GwexObs(variable='Prec',date=vecDates,obs=dailyPrecipGWEX[,1:2])

# Example of a data.frame which can be used for 'xiHat'. For each month, xi values 
#can be prescribed, for example using a regionalisation method.
xiReg = data.frame(station=c('S1','S2','S3'),JAN=c(0,0,0.06),FEB=c(0,0,0.06),
MAR=c(0,0,0.01),APR=c(0,0,0.01),MAY=c(0,0,0.01),
JUN=c(0,0.01,0.14),JUL=c(0,0.01,0.14),AUG=c(0,0.01,0.14),
SEP=c(0,0,0.02),OCT=c(0,0,0.02),NOV=c(0,0,0.02),
DEC=c(0,0,0.06))

# Options: specify the threshold for precipitation (0.5 mm) to distinguish wet and 
# dry states (th), xi values for the DGPD distribution (xiHat), a Student copula for 
# the spatial dependence (copulaInt), a model based on 3-day aggregated values 
# (is3Damount), a MAR(1) process (isMAR), a EGPD distribution for marginal intensities
# ('typeMargin'), and 200 replicates for the runs used during the fitting process
# (this value being 100,000 by default, in order to obtain a reasonable precision of the 
# estimates) 
list.options = list(th=0.5,nLag=1,xiHat=xiReg,copulaInt='Student',is3Damount=TRUE,isMAR=TRUE,
typeMargin='EGPD',nChainFit=200)

# Fit precipitation model
myParPrec = fitGwexModel(myObsPrec,list.options) # fit model
myParPrec # print object

###############################################################
#     FIT THE TEMPERATURE MODEL, COND. TO PRECIPITATION
###############################################################
# Format observations: create a G-Wex object
myObsTemp = GwexObs(variable='Temp',date=vecDates,obs=dailyTemperGWEX)

# Fit temperature model with a long-term linear trend ('hasTrend'), Gaussian margins 
# ('typeMargin') and Gaussian spatial dependence ('depStation')
myParTemp = fitGwexModel(myObsTemp,listOption=list(hasTrend=TRUE,typeMargin='Gaussian',
depStation='Gaussian'))
myParTemp # print object
# }