This function makes an iteration of PCA-Gaussianization process

1 | ```
GPCA_iteration(x_prev, extremes = TRUE)
``` |

`x_prev` |
previous set of random variable |

`extremes` |
see |

A `GPCA_iteration`

S3 object which contains the following objects:

`x_prev`

Previous set of random variable, `x_prev`

input variable

`x_gauss_prev`

Marginal Gaussianization of `x_prev`

obtained through `normalizeGaussian_severalstations`

`B_prev`

rotation matrix (i. e. eigenvector matrix of the covariance matrix of `x_gauss_prev`

`x_next`

results obtained by multiplying `B_prev`

by `x_gauss_prev`

(see equation 1 of the reference)

This function is based on equation (1) of "PCA Gaussianization for One-Class Remote Sensing Image" by V. Laparra et al., www.uv.es/lapeva/papers/SPIE09_one_class.pdf and http://ieeexplore.ieee.org/document/5413808/

Emanuele Cordano

`GPCA`

,`GPCA_iteration`

,`inv_GPCA_iteration`

,`inv_GPCA`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
library(RMAWGEN)
set.seed(1222)
N <- 20
x <- rexp(N)
y <- x+rnorm(N)
df <- data.frame(x=x,y=y)
GPCA <- GPCA_iteration(df,extremes=TRUE)
x <- rnorm(N)
y <- x+rnorm(N)
dfn <- data.frame(x=x,y=y)
GPCAn <- GPCA_iteration(dfn,extremes=TRUE)
``` |

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