This function extends continuity_ratio and adds the corresponding gaussian correlation matrix for no-precipitation occurrence.

Description

This function extends continuity_ratio and adds the corresponding gaussian correlation matrix for no-precipitation occurrence.

Usage

CCGamma(
  data,
  lag = 0,
  p0_v1 = NULL,
  p = NA,
  valmin = 0.5,
  nearPD = (lag >= 0),
  interval = c(-1, 1),
  tolerance = .Machine$double.eps,
  only.matrix = FALSE,
  return.value = NULL,
  null.gcorrelation = 1e-05,
  sample = NULL,
  origin = "1961-1-1",
  ...
)

Arguments

Value

An object which is a list containing the following fields:

continuity_ratio : lag-day lagged continuity ratio, as returned by continuity_ratio;

occurrence : joint probability of lag-day lagged precipitation occurrence, as returned by continuity_ratio;

nooccurrence : joint probability of lag-day lagged no precipitation occurrence, as returned by continuity_ratio;

lag : number of days lagged between the two compared events (see argument lag);

p0_v1 : vector of marginal probability of no precipitation occurrence. If lag is 0, it corresponds to the diagonal of nooccurrence matrix (see argument p0_v1);

nooccurrence_gcorrelation corresponding gaussian correlation for no precipitation occurrence obtained by applying omega_inv to nooccurrence,

If the argument only.matrix is TRUE, only nooccurrence_gcorrelation is returned as a matrix. In case the argument lag is a vector wirh length more than one, the function returns a list of the above-cited return object for each value of the vector lag.

Note

This functon is useful to generate the serial cross-correlation matrices for no precipitation occurrence for Yule-Walker Equations. In case lag is a vactor, nearPD must be a vector of the same size, default is (lag==0).

See the R code for major details

Author(s)

Emanuele Cordano

References

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

See Also

continuity_ratio,omega_inv,omega,CCGammaToBlockmatrix

Examples

data(trentino)

year_min <- 1961
year_max <- 1990
origin <- paste(year_min,1,1,sep="-")

period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
station <- names(PRECIPITATION)[!(names(PRECIPITATION) %in% c("day","month","year"))]
prec_mes <- PRECIPITATION[period,station]  

## removing nonworking stations (e.g. time series with NA)
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
		 accepted[it]  <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it]))
}

prec_mes <- prec_mes[,accepted]
## the dateset is reduced!!! 
prec_mes <- prec_mes[,1:2]

CCGamma <- CCGamma(data=prec_mes,lag=0,tolerance=0.001,only.matrix=FALSE)

## Not Run in the examples, uncomment to run the following line
 CCGamma <- CCGamma(data=prec_mes,lag=0:2,tolerance=0.001,only.matrix=FALSE)

## Not Run in the examples, uncomment to run the following line
 CCGamma_monthly <- CCGamma(data=prec_mes,lag=0,tolerance=0.001,only.matrix=FALSE,
                     sample="monthly",origin=origin)

This function returns a blockmatrix object containing the gaussian cross-correlation matrices.

Description

This function returns a blockmatrix object containing the gaussian cross-correlation matrices.

Usage

CCGammaToBlockmatrix(data, lag = 0, p = 3, ...)

Arguments

Details

This a wrapper for CCGamma with the option only.matrix=TRUE and the function value is transformed into a blockmatrix object.

Value

A blockmatrix object containing the gaussian cross-correlation matrices.

See Also

CCGamma,continuity_ratio,omega_inv,omega

Examples

data(trentino)

year_min <- 1961
year_max <- 1990

period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
station <- names(PRECIPITATION)[!(names(PRECIPITATION) %in% c("day","month","year"))]
prec_mes <- PRECIPITATION[period,station]  

## removing nonworking stations (e.g. time series with NA)
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
		 accepted[it]  <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it]))
}

prec_mes <- prec_mes[,accepted]
## the dateset is reduced!!! 
prec_mes <- prec_mes[,1:2]

p <- 1 ## try p <- 2 !!!  
CCGamma <- CCGammaToBlockmatrix(data=prec_mes,lag=0,p=p,tolerance=0.001)

## Not Run in the examples, uncomment to run the following line
 CCGamma_1 <- CCGammaToBlockmatrix(data=prec_mes,lag=1,p=p,tolerance=0.001)


### Alternatively, recommended ..... 
## Not Run in the examples, uncomment to run the following line
 CCGamma <- CCGammaToBlockmatrix(data=prec_mes,lag=0,p=p+1,tolerance=0.001)

 CCGamma0 <- CCGamma[1:p,1:p]
 CCGamma1 <- CCGamma[(1:p),(1:p)+1]

 CCGamma0_inv <- solve(CCGamma0)


## Not Run in the examples, uncomment to run the following line
a1 <- blockmatmult(CCGamma0,CCGamma0_inv)
a2 <- blockmatmult(CCGamma1,CCGamma0_inv)



CCGamma_1t <- t(CCGamma1)
CCGamma_0t <- t(CCGamma0)

 A <- t(solve(CCGamma_0t,CCGamma_1t))

Creates a Precipitation Amount Model

Description

Creates a Precipitation Amount Model

Usage

PrecipitationAmountModel(
  x,
  valmin = 1,
  station = names(x),
  sample = "monthly",
  origin = "1961-1-1",
  ...
)

Arguments

Value

The function returns AN S3 OBJECT ...... the correlation matrix of precipitation amount values (excluding the zeros). In case sample=="monthly" the runction return a MonlthyList S3 object.

See Also

predict.PrecipitationAmountModel,normalizeGaussian_severalstations,generate

Examples

set.seed(1245)

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"))]

precamount <- PrecipitationAmountModel(prec_mes,station=station,origin=origin)

val <- predict(precamount)

prec_gen <- generate(precamount)  




month <- adddate(as.data.frame(residuals(precamount$T0090)),origin=origin)$month
#####plot(month,residuals(precamount$T0090))
plot(factor(month),residuals(precamount$T0090))

qqplot(prec_mes$T0083,prec_gen$T0083)
abline(0,1)


## SINGLE STATION

station <- "T0083"

precamount_single <- PrecipitationAmountModel(prec_mes,station=station,origin=origin)

val_single <- predict(precamount_single)

prec_gen_single <- generate(precamount_single)  



month <- adddate(as.data.frame(residuals(precamount_single[[station[1]]])),origin=origin)$month
plot(factor(month),residuals(precamount_single[[station[1]]]))


### Comparison (Q-Q plot) between multi and single sites. 

qqplot(prec_mes$T0083,prec_gen$T0083,col=1)
abline(0,1)
points(sort(prec_mes$T0083),sort(prec_gen_single$T0083),pch=2,col=2)
legend("bottomright",pch=c(1,2),col=c(1,2),legend=c("Multi Sites","Single Site"))







abline(0,1)

Precipitation Occurrence Model

Description

This functions creates a stochastic Occurrence Model for the variable x (PrecipitationOccurrenceModel S3 object) through a calibration from observed data.

Usage

PrecipitationOccurrenceModel(
  x,
  exogen = NULL,
  p = 1,
  monthly.factor = NULL,
  valmin = 0.5,
  id.name = NULL,
  ...
)

Arguments

Value

The function returns a PrecipitationOccurrenceModel-class S3 object containing the following elements:

predictor data frame containg the endogenous and exogenous predictors of the logistic regression model;

glm the genaralized liner model using for the logistic regression;

p auto-regression order

valmin minimum admitted value for daily precipitation amount

See Also

glm

Examples

library(RGENERATEPREC)

data(trentino)

year_min <- 1961
year_max <- 1990

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)
model <- PrecipitationOccurrenceModel(x=prec_mes[,it],exogen=vect,monthly.factor=months)

probs <- predict(model$glm,type="response")





plot(months[-1],probs)

newdata <- model$predictor[2000:2007,]
probs0 <- predict(model,newdata=newdata)

Precipitation Occurrence Multi-Site Model

Description

This functions creates a stochastic Occurrence Multi-Site Model for the variable x (PrecipitationOccurrenceMultiSiteModel S3 object) through a calibration from observed data.

Usage

PrecipitationOccurrenceMultiSiteModel(
  x,
  exogen = NULL,
  station = names(x),
  origin = origin,
  valmin = 0.5,
  multisite_type = "wilks",
  tolerance_wilks = 0.001,
  p = 2,
  ...
)

Arguments

Value

The function returns a PrecipitationOccurrenceModel-class S3 object containing the following elements:

... PrecipitationOccurrenceModel S3 class objects for each analyzed site. The name is the site (or station) code

ccgama CCGammaObjectListPerEachMonth object, i.e. matices of Gaussian Inter-Site Correlation returned by CCGamma;

type string indicating the utilized approach for spatial multi-site dependence description, only "wilks" type is implemented;

station character string vectors containing the codes of the station used in PrecipitationMultiSiteOccurrenceModel.

See Also

PrecipitationOccurrenceModel,CCGamma

Examples

library(RGENERATEPREC)

data(trentino)

year_min <- 1961
year_max <- 1990
origin <- paste(year_min,1,1,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"))]
station <- station[1:2] # to save example elapsed time!!
exogen <- Tx_mes-Tn_mes
months <- factor(prec_mes$month)

#' ### Not Run!! 
# The following lines are commented to save example elapsed time!!
model_multisite <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,exogen=exogen,
origin=origin,multisite_type="wilks")

### Not Run!! 
#  The following lines are commented to save example elapsed time!!
model_multisite_logit <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,exogen=exogen,
origin=origin,multisite_type="logit")
 

It calculates dry/wet spell duration.

Description

It calculates dry/wet spell duration.

Usage

dw.spell(
  data,
  valmin = 0.5,
  origin = "1961-1-1",
  extract = NULL,
  month = 1:12,
  melting.df = FALSE,
  from.start = FALSE,
  only.inner = FALSE
)

Arguments

Value

Function returns a list of data frames containing the spell length expressed in days

Examples

 


data(trentino)

year_min <- 1961
year_max <- 1990

period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
station <- names(PRECIPITATION)[!(names(PRECIPITATION) %in% c("day","month","year"))]
prec_mes <- PRECIPITATION[period,station]  

## removing nonworking stations (e.g. time series with NA)
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
		 accepted[it]  <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it]))
}

prec_mes <- prec_mes[,accepted]
## the dateset is reduced!!! 
prec_mes <- prec_mes[,1:3]

origin <- paste(year_min,1,1,sep="-")
dw_spell <- dw.spell(prec_mes,origin=origin)
dw_spell_dry <- dw.spell(prec_mes,origin=origin,extract="dry")

hist(dw_spell_dry$T0001$spell_length)


## Single Gauging Station

prec_mes <- prec_mes[,1]

origin <- paste(year_min,1,1,sep="-")
dw_spell <- dw.spell(prec_mes,origin=origin)
dw_spell_dry <- dw.spell(prec_mes,origin=origin,extract="dry")
dw_spell_dry_start <- dw.spell(prec_mes,origin=origin,extract="dry",
	month=5:8,from.start=TRUE) ## dry spell 
	dw_spell_dry_start_2 <- dw.spell(prec_mes,origin=origin,extract="dry",
month=5:8,from.start=TRUE,only.inner=TRUE) ## dry spell 
## is referenced to the first day instead of the latest one as default. 

hist(dw_spell_dry[[1]]$spell_length)

Stochastic Generation of a PrecipitationOccurrenceModel or PrecipitationOccurrenceMultiSiteModel model object

Description

It is an implentation of generate method

Usage

## 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 = 10,
  origin = "1961-1-1",
  end = "1990-1-1",
  previous = NULL,
  monthly.factor = NULL,
  ...
)

## S3 method for class 'PrecipitationAmountModel'
generate(x, ...)

Arguments

Value

A vector or a data frame reporting generated time series for each station.

References

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

See Also

generate,predict.glm,PrecipitationOccurrenceModel,PrecipitationOccurrenceMultiSiteModel

Examples

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)
## 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)

 

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))


### MultiSite Generation 


station <- station[1:2]
exogen <- Tx_mes[,station]-Tn_mes[,station]

months <- factor(prec_mes$month)



 
model_multisite <- 
PrecipitationOccurrenceMultiSiteModel(x=prec_mes[,station],
exogen=exogen,origin=origin,multisite_type="wilks")


## 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)

gen_multisite_logit <- generate(model_multisite_logit,exogen=exogen,origin=origin,end=end)

It calculates the number of wet days for each month and each year

Description

It calculates the number of wet days for each month and each year

Usage

nwetdays(data, valmin = 0.5, origin = "1961-1-1", station = names(data))

Arguments

Value

Function returns a list of data frames containing the spell length expressed in days

Examples

 


data(trentino)

year_min <- 1961
year_max <- 1990

period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
station <- names(PRECIPITATION)[!(names(PRECIPITATION) %in% c("day","month","year"))]
prec_mes <- PRECIPITATION[period,station]  

## removing nonworking stations (e.g. time series with NA)
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
		 accepted[it]  <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it]))
}

prec_mes <- prec_mes[,accepted]
## the dateset is reduced!!! 
prec_mes <- prec_mes[,1:3]

origin <- paste(year_min,1,1,sep="-")

nwetdays <- nwetdays(prec_mes,origin)

This function finds the bivariate joint probability or the binary correlation from the corresponding Gaussian correlation x

Description

This function finds the bivariate joint probability or the binary correlation from the corresponding Gaussian correlation x

Usage

omega(x = 0.5, p0_v1 = 0.5, p0_v2 = NA, correlation = FALSE)

Arguments

Value

probability of no precipitation occurrence in both v1 and v2 simultaneously. It is a matrix if x is a matrix.

Note

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.

Author(s)

Emanuele Cordano

References

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

See Also

normalCopula,pcopula

Examples

rho <- 0.4
p00 <- omega(x=rho,p0_v1=0.5,p0_v2=0.5)
cor00 <- omega(x=rho,p0_v1=0.5,p0_v2=0.5,correlation=TRUE)

This function is the inverse of omega function

Description

This function is the inverse of omega function

Usage

omega_inv(
  p0 = NULL,
  p0_v1 = 0.5,
  p0_v2 = p0_v1,
  p00 = p0_v1 * p0_v2,
  correlation = NA,
  only.value = TRUE,
  interval = c(-1, 1),
  tolerance = 0.001,
  nearPD = TRUE,
  force.independence = TRUE,
  ...
)

Arguments

Value

value of expected correlation between the corresponding Gaussian-distributed variables (see x input argument of omega.

Note

This function finds the zero of the omega_root function by calling uniroot. If the argument p0 is not NULL and is a matrix of joint probabilities, the function returns a correlation matrix by using the elements of p0 ass joint probabilities for each couple and p0_v1 as a vector of marginal probability of each occurrence/no-occurrence (In this case if the length of p0_v1 does not correspond to the number of columns of p0, the marginal probabilities are taken from the diagonal of p0). See the R code for major details.

Author(s)

Emanuele Cordano

See Also

normalCopula,pcopula,omega(and reference URLs therein)

Examples

x <- omega_inv(p0_v1=0.5,p0_v2=0.5,p00=1.1*0.5*0.5)
omega(x,p0_v1=0.5,p0_v2=0.5)

This is the target function whose zero is searched to crete the inverse function of omega.

Description

This is the target function whose zero is searched to crete the inverse function of omega.

Usage

omega_root(
  x = 0.5,
  p0_v1 = 0.5,
  p0_v2 = 0.5,
  p00 = p0_v1 * p0_v2,
  correlation = NA
)

Arguments

Value

the value p00-omega(x=x,p0_v1=p0_v1,p0_v2=p0_v2) or correlation-omega(x=x,p0_v1=p0_v1,p0_v2=p0_v2) (if correlation is not NA)

Note

This function makes use of normal copula

Author(s)

Emanuele Cordano

See Also

normalCopula,pcopula,omega,omega_inv

Examples

rho <- 0.4
p00 <- omega(x=rho,p0_v1=0.5,p0_v2=0.5)
omega_root(x=rho,p0_v1=0.5,p0_v2=0.5,p00=p00)

Prediction of a PrecipitationOccurrenceModel model object

Description

It is a wrapper of predict.glm method for the a PrecipitationOccurrenceModel model object S3 class.

Usage

## S3 method for class 'PrecipitationOccurrenceModel'
predict(
  object,
  newdata = NULL,
  type = "response",
  previous = NULL,
  endogenous = NULL,
  ...
)

## S3 method for class 'PrecipitationOccurrenceMultiSiteModel'
predict(object, ...)

## S3 method for class 'PrecipitationAmountModel'
predict(
  object,
  newdata = NULL,
  origin_newdata = NA,
  precipitation.value.random.generation = FALSE,
  ...
)

Arguments

Value

A vector or a data frame reporting predicted time series for each station.

See Also

predict.glm,PrecipitationOccurrenceModel

predict.glm,predict.glm,PrecipitationOccurrenceModel,PrecipitationAmountModel

Examples

library(RGENERATEPREC)

data(trentino)

year_min <- 1961
year_max <- 1990

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]
origin <- paste(year_min,1,1,sep="-")


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)
model <- PrecipitationOccurrenceModel(x=prec_mes[,it],exogen=vect,monthly.factor=months)

probs <- predict(model)

nday <- 3.0
vect_new <- array(1.0,nday)
months_new <- array(1,nday)
row_test <- 2000:2007
newdata <- model$predictor[row_test,]
probs2 <- predict(model,newdata=newdata)

probs[row_test]==probs2
###





prec_occurrence_mes <- prec_mes>=valmin

station <- names(prec_mes)[!(names(prec_mes) %in% c("day","month","year"))]

station <- station[1:4] ## reduced the dataset!!! 
Tx_mes <- Tx_mes[,station]
Tn_mes <- Tn_mes[,station]

prec_mes <- prec_mes[,station]
exogen <- Tx_mes-Tn_mes
months <- factor(prec_mes$month)


### Not Run 
### Please uncomment the following lines to run them 


model_multisite <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,
exogen=exogen,origin=origin,multisite_type="wilks")


model_multisite_logit <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,
exogen=exogen,origin=origin,multisite_type="logit")


probs_multimodel  <- predict(model_multisite_logit)