Prediction of a PrecipitationOccurrenceModel model object

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

# 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

object

model returned by PrecipitationOccurrenceModel

newdata

predictor or exogenous variables

type

see predict.glm. Default is "response". See predict.glm.

previous

logical vector containing previously occurred states.

endogenous

String vector containing the name of the endogenous variables. It is used if the endogenous variables are more than one, otherwise is set NULL(Default).

...

further arguments

origin_newdata

character string containing the date corresponding the first row of newdata

precipitation.value.random.generation

logical value. If it is FALSE (Default) the method predict.PrecipitationAmountModel returns conditioned random values, otherwise these values are converted to precipitation values through their observed non-parametric distributions.

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
#> 2000 2001 2002 2003 2004 2005 2006 2007 
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE 
###





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)

# \donttest{
### Not Run 
### Please uncomment the following lines to run them 


model_multisite <- PrecipitationOccurrenceMultiSiteModel(x=prec_mes,
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!!!


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


probs_multimodel  <- predict(model_multisite_logit)

# }