predict function

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.

Returns

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

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)

See Also

predict.glm,PrecipitationOccurrenceModel

predict.glm,predict.glm,PrecipitationOccurrenceModel,PrecipitationAmountModel