Precipitation Occurence Model

Description

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

Usage

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

Arguments

Value

The function returns a PrecipitationOccurenceModel-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_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$glm,type="response")





plot(months[-1],probs)

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

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/PrecipitationOccurenceModel.Rd_%03d_medium.png", width=480, height=480)
> ### Name: PrecipitationOccurenceModel
> ### Title: Precipitation Occurence Model
> ### Aliases: PrecipitationOccurenceModel
> 
> ### ** 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_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$month)
> model <- PrecipitationOccurenceModel(x=prec_mes[,it],exogen=vect,monthly.factor=months)
> 
> probs <- predict(model$glm,type="response")
> 
> 
> 
> 
> 
> plot(months[-1],probs)
> 
> newdata <- model$predictor[2000:2007,]
> probs0 <- predict(model,newdata=newdata)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>