DEPRECATED Converts several samples x random variable (daily precipitation values) extracted by populations represented by the columns of data respectively or sample to a normally-distributed samples with assinged mean and standard deviation or vice versa in case inverse is TRUE using the function normalizeGaussian_prec

Description

DEPRECATED Converts several samples x random variable (daily precipitation values) extracted by populations represented by the columns of data respectively or sample to a normally-distributed samples with assinged mean and standard deviation or vice versa in case inverse is TRUE using the function normalizeGaussian_prec

Usage

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normalizeGaussian_severalstations_prec(x, data = x, cpf = NULL, mean = 0,
  sd = 1, inverse = FALSE, qnull = NULL, valmin = 0.5, type = 3,
  extremes = TRUE, sample = NULL, origin_x = NULL, origin_data = NULL)

Arguments

x

value 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.

qnull

probability of no precipitation occurence. (It can be a matrix in case sample="monthly"

valmin

minimum value of precipitation to consider a wet day

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

information about sample or probability distribution. Default is NULL

origin_x

date corresponding to the first row of x

origin_data

date corresponding to the first row of data

Value

a matrix or a data.frame with 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_prec

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