normalizeGaussian_prec: Converts precipitation values to "Gaussinized"...

In RMAWGEN: Multi-Site Auto-Regressive Weather GENerator

Description

Converts precipitation values to "Gaussinized" normally-distributed values taking into account the probability of no precipitation occurrences. values or vice versa in case inverse is TRUE

Usage

normalizeGaussian_prec(x = 0, data = x, cpf = NULL, mean = 0,
  sd = 1, inverse = FALSE, type = 3, extremes = TRUE,
  sample = NULL, qnull = 0, valmin = 1)

Arguments

x	value or vector of values to be converted
data	a sample of data on which a non-parametric probability distribution is estimated
cpf	cumulative probability distribution. If NULL (default) is calculated as ecdf(data)
mean	mean (expected value) of the normalized random variable. Default is 0.
sd	standard deviation of the normalized random variable. Default is 1.
inverse	logical value. If TRUE the function works inversely (the opposite way). Default is FALSE.
type	see quantile
extremes	logical variable. If TRUE (default) the probability or frequency is multiplied by \frac{N}{N+1} where N is the length of data
sample	a character string or NULL containing sample or probability distribution information. Default is NULL
qnull	probability of no precipitation occurrence
valmin	minimum value of precipitation to consider a wet day

Value

the normalized variable or its inverse

Note

In the version 1.2.5 of RMAWGEN This function is deprecated and not used.

Author(s)

Emanuele Cordano, Emanuele Eccel

See Also

normalizeGaussian

Examples

library(RMAWGEN)
NDATA <- 1000
occurrence <- as.logical(runif(NDATA)>0.5)
prec <- rexp(NDATA,rate=1/3)
prec[!occurrence] <- 0
valmin <- 0.5 #0.01
x <- normalizeGaussian_prec(x=prec,valmin=valmin)
prec2 <- normalizeGaussian_prec(x=x,data=prec,valmin=valmin,inverse=TRUE)
qqplot(prec,prec2)

occurrence3 <- as.logical(runif(NDATA)>0.5)
prec3 <- rexp(NDATA,rate=1/3)
prec3[!occurrence3] <- 0
x3 <- normalizeGaussian_prec(x=prec3,valmin=valmin) 

qqplot(x,x3)
abline(0,1)

RMAWGEN documentation built on Dec. 12, 2019, 9:07 a.m.