This function makes a Gaussianization procedure based on PCA iteration ( see GPCA_iteration)

This function makes a Gaussianization procedure based on PCA iteration ( see GPCA_iteration)

GPCA(x_prev, n = 30, extremes = TRUE, nearPD = FALSE)

Arguments

x_prev

previous set of the random variable x. If it is a varest object, the residuals are taken into account.

n

number of reiterations

extremes

see normalizeGaussian_severalstations

nearPD

logical. Default is FALSE. If TRUE covariance matrix is corrected through Nearest Positive Definite Matrix procedure, i.e. nearPD

Value

A GPCA-class S3 object returned by GPCA_iteration at each iteration and the final results of the G-PCA procedure (matrix final_results)

Note

This function re-iterates the equation (1) of "PCA Gaussianization for One-Class Remote Sensing Image" by V. Laparra et al., https://www.uv.es/lapeva/papers/SPIE09_one_class.pdf,https://www.uv.es/vista/vistavalencia/papers/SPIE_09_Gaussianization_presentation.pdf

See also

GPCA,GPCA_iteration,inv_GPCA_iteration,inv_GPCA,GPCA-class for 'GPCA' S3 class

Author

Emanuele Cordano

Examples

library(RMAWGEN)
set.seed(1222)
nIterations <- 30
N <- 20
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)

GPCA <- GPCA(df,n=nIterations,extremes=TRUE)

x <- rnorm(N)
y <- x+rnorm(N)
dfn <- data.frame(x=x,y=y)

GPCAn <- GPCA(dfn,n=nIterations,extremes=TRUE)