RMAWGEN (version 1.3.7)

normalizeGaussian_prec: 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

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

See Also

normalizeGaussian

Examples

# NOT RUN {
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)


# }