normalizeGaussian_severalstations: Converts several samples 'x' random variable extracted by...
In RMAWGEN: Multi-Site Auto-Regressive Weather GENerator

Description Usage Arguments Value Note Author(s) See Also Examples

View source: R/normalizeGaussian_sevaralstations.R

Converts several samples x random variable 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

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normalizeGaussian_severalstations(x, data = x, cpf = NULL, mean = 0,
  sd = 1, inverse = FALSE, step = NULL, prec = 10^-4, type = 3,
  extremes = TRUE, sample = NULL, origin_x = NULL, origin_data = NULL)

`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`.
`step`	vector of values in which step discontinuities of the cumulative probability function occur. Default is `NULL`
`prec`	amplitude of the neighbourhood of the step discontinuities where cumulative probability function is treated as non-continuous.
`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 on how to sample `x` and `data`. Default is `NULL`, this means that the values of each column of `x` and `data` belong to the same sample. If `x` and `data` are sampled for each month seperately, it is set to `monthly`.
`origin_x`	date corresponding to the first row of `x`
`origin_data`	date corresponding to the first row of `data`

a matrix with the normalized variable or its inverse

It applies normalizeGaussian for each column of x and data. See the R code for further details

Emanuele Cordano, Emanuele Eccel

normalizeGaussian

library(RMAWGEN) 
N <- 30
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

dfg <- normalizeGaussian_severalstations(df,data=df,extremes=TRUE,inverse=FALSE)

dfi <- normalizeGaussian_severalstations(dfg,data=df,extremes=TRUE,inverse=TRUE)