View source: R/normalizeGaussian_sevaralstations.R
normalizeGaussian_severalstations | R Documentation |
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
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 = origin_x
)
x |
value to be converted |
data |
a sample of data on which a non-parametric probability distribution is estimated |
cpf |
cumulative probability distribution. If |
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 |
step |
vector of values in which step discontinuities of the cumulative probability function occur. Default is |
prec |
amplitude of the neighbourhood of the step discontinuities where cumulative probability function is treated as non-continuous. |
type |
see |
extremes |
logical variable.
If
where |
sample |
information on how to sample |
origin_x |
date corresponding to the first row of |
origin_data |
date corresponding to the first row of |
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
## 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)
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