PrecipitationOccurrenceModel or PrecipitationOccurrenceMultiSiteModel model objectR/generate.PrecipitationAmountModel.R, R/generate.PrecipitationOccurenceModel.R, R/predict.zzgeneratezPrecipitationAmountModel.R
generate.RdIt is an implentation of generate method
# S3 method for class 'PrecipitationAmountModel'
generate(x, ...)
# S3 method for class 'PrecipitationOccurrenceModel'
generate(
x,
newdata = NULL,
previous = NULL,
n = 30,
random = runif(n, min = 0, max = 1),
exogen = NULL,
monthly.factor = NULL,
...
)
# S3 method for class 'CCGammaObjectListPerEachMonth'
generate(x, ...)
# S3 method for class 'PrecipitationOccurrenceMultiSiteModel'
generate(
x,
exogen,
n = NA,
origin = "1961-1-1",
end = "1990-1-1",
previous = NULL,
monthly.factor = NULL,
...
)
# S3 method for class 'PrecipitationAmountModel'
generate(x, ...)model returned by PrecipitationOccurrenceModel or PrecipitationOccurrenceMultiSiteModel
further arguments
predictor or exogenous variables. See predict.PrecipitationOccurrenceModel
logical vector containing previously occurred states
number of generations. See generate. Here it is ignored and the number of generations is given by origin,end or monthly.factor.
vector of random or calculated numbers ranging between 0 and 1
predictor or exogenous variables
vector of factors indicating the month of the days
character strings (yyyy-dd-mm) indicating the start and/or end date of the daily weather generation.
A vector or a data frame reporting generated time series for each station.
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/10.1002/joc.2305
library(RGENERATEPREC)
## A function example can be found in the following script file:
scriptfile <- system.file("example.generate.R",package="RGENERATEPREC")
## The corrent file path is given by 'scriptfile' variable:
print(scriptfile)
#> [1] "/tmp/RtmpD5u1hn/temp_libpath3094d53627f286/RGENERATEPREC/example.generate.R"
## To run the example file, launch the file with 'source' command (uncomment the following line)
#source(scriptfile)
## ALTERNATIVELY you can run the following lines:
data(trentino)
year_min <- 1961
year_max <- 1990
origin <- paste(year_min,1,1,sep="-")
end <- paste(year_max,12,31,sep="-")
period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
period_temp <- TEMPERATURE_MAX$year>=year_min & TEMPERATURE_MAX$year<=year_max
prec_mes <- PRECIPITATION[period,]
Tx_mes <- TEMPERATURE_MAX[period_temp,]
Tn_mes <- TEMPERATURE_MIN[period_temp,]
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
acc <- TRUE
acc <- (length(which(!is.na(Tx_mes[,it])))==length(Tx_mes[,it]))
acc <- (length(which(!is.na(Tn_mes[,it])))==length(Tn_mes[,it])) & acc
accepted[it] <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it])) & acc
}
valmin <- 1.0
prec_mes <- prec_mes[,accepted]
Tx_mes <- Tx_mes[,accepted]
Tn_mes <- Tn_mes[,accepted]
prec_occurrence_mes <- prec_mes>=valmin
station <- names(prec_mes)[!(names(prec_mes) %in% c("day","month","year"))]
it <- station[2]
vect <- Tx_mes[,it]-Tn_mes[,it]
months <- factor(prec_mes$month)
# \donttest{
model <-
PrecipitationOccurrenceModel(x=prec_mes[,it],exogen=vect,
monthly.factor=months,valmin=valmin)
obs <- prec_mes[,it]>=valmin
gen <- generate(model,exogen=vect,monthly.factor=months,n=length(months))
# }
## Only 10 generated realizations!!
gen10 <- generate(model,exogen=vect,monthly.factor=months,n=10)
### MultiSite Generation
station <- station[1:2]
exogen <- Tx_mes[,station]-Tn_mes[,station]
months <- factor(prec_mes$month)
# \donttest{
model_multisite <-
PrecipitationOccurrenceMultiSiteModel(x=prec_mes[,station],
exogen=exogen,origin=origin,multisite_type="wilks")
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
#> lag
#> 0
#> Hmm... p0 - first argument - must be a matrix of probabilities!!!
## LOGIT-type Model
model_multisite_logit <-
PrecipitationOccurrenceMultiSiteModel(x=prec_mes,exogen=exogen,
origin=origin,multisite_type="logit",station=station)
obs_multisite <- prec_mes[,station]>=valmin
gen_multisite <- generate(model_multisite,exogen=exogen,origin=origin,end=end)
#> Processing T0001
#> Processing T0014
gen_multisite_logit <- generate(model_multisite_logit,exogen=exogen,origin=origin,end=end)
#> Processing: 5.00%
#> Processing: 10.00%
#> Processing: 15.00%
#> Processing: 20.00%
#> Processing: 25.00%
#> Processing: 30.00%
#> Processing: 35.00%
#> Processing: 40.00%
#> Processing: 45.00%
#> Processing: 50.00%
#> Processing: 55.00%
#> Processing: 60.00%
#> Processing: 65.00%
#> Processing: 70.00%
#> Processing: 75.00%
#> Processing: 80.00%
#> Processing: 85.00%
#> Processing: 90.00%
#> Processing: 95.00%
#> Processing: 100.00%
# }