normalizeGaussian_severalstations function

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

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)

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.
• 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

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

Returns

a matrix with the normalized variable or its inverse

Note

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

Examples

## Not run:
 
library(RMAWGEN) 

set.seed(1234)
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)

N <- 365*2
origin <- "1981-01-01"
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

dfgm <- normalizeGaussian_severalstations(df,data=df,extremes=TRUE,
inverse=FALSE,origin_x=origin,origin_data=origin,sample="monthly")

dfim <- normalizeGaussian_severalstations(dfg,data=df,extremes=TRUE,
inverse=TRUE,origin_x=origin,origin_data=origin,sample="monthly")
 
 
## Compatibility with 'lubridate' package

library(lubridate)

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)
 
N <- 365*2
origin <- "1981-01-01"
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)
 
dfgm <- normalizeGaussian_severalstations(df,data=df,extremes=TRUE,
inverse=FALSE,origin_x=origin,origin_data=origin,sample="monthly")
 
dfim <- normalizeGaussian_severalstations(dfg,data=df,extremes=TRUE,
inverse=TRUE,origin_x=origin,origin_data=origin,sample="monthly")
## End(Not run)

See Also

normalizeGaussian

Author(s)

Emanuele Cordano, Emanuele Eccel