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Last data update: 2014.03.03

R: Prediction of a 'PrecipitationOccurenceModel' model object

predict.PrecipitationOccurenceModel

R Documentation

Prediction of a `PrecipitationOccurenceModel` model object

Description

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

Usage

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

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

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

Arguments

`object`	model returned by `PrecipitationOccurenceModel`
`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.

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_occurence_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 <- PrecipitationOccurenceModel(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_occurence_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 <- PrecipitationOccurenceMultiSiteModel(x=prec_mes,
#exogen=exogen,origin=origin,multisite_type="wilks")
#
#
#model_multisite_logit <- PrecipitationOccurenceMultiSiteModel(x=prec_mes,
#exogen=exogen,origin=origin,multisite_type="logit")
#
#
#probs_multimodel  <- predict(model_multisite_logit)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RGENERATEPREC)
Loading required package: copula
Loading required package: RGENERATE
Loading required package: RMAWGEN
Loading required package: chron
Loading required package: date
Loading required package: vars
Loading required package: MASS
Loading required package: strucchange
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: urca
Loading required package: lmtest

Attaching package: 'vars'

The following object is masked from 'package:copula':

    A

Loading required package: blockmatrix
Loading required package: Matrix
Loading required package: stringr

Attaching package: 'stringr'

The following object is masked from 'package:strucchange':

    boundary

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RGENERATEPREC/predict.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predict.PrecipitationOccurenceModel
> ### Title: Prediction of a 'PrecipitationOccurenceModel' model object
> ### Aliases: predict predict.PrecipitationAmountModel
> ###   predict.PrecipitationOccurenceModel
> ###   predict.PrecipitationOccurenceMultiSiteModel
> 
> ### ** 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_occurence_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$month)
> model <- PrecipitationOccurenceModel(x=prec_mes[,it],exogen=vect,monthly.factor=months)
> 
> probs <- predict(model)
> 
> nday <- 3.0
> vect_new <- array(1.0,nday)
> months_new <- array(1,nday)
> row_test <- 2000:2007
> newdata <- model$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 FALSE  TRUE 
> ###
> 
> 
> 
> 
> 
> prec_occurence_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 <- PrecipitationOccurenceMultiSiteModel(x=prec_mes,
> #exogen=exogen,origin=origin,multisite_type="wilks")
> #
> #
> #model_multisite_logit <- PrecipitationOccurenceMultiSiteModel(x=prec_mes,
> #exogen=exogen,origin=origin,multisite_type="logit")
> #
> #
> #probs_multimodel  <- predict(model_multisite_logit)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>