1d:T5f5,# This function makes a Gaussianization procedure based on PCA iteration ( see `GPCA_iteration`) ```r 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` ## Returns 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/lapeva/papers/SPIE09_one_class.pdf),[https://www.uv.es/vista/vistavalencia/papers/SPIE_09_Gaussianization_presentation.pdf](https://www.uv.es/vista/vistavalencia/papers/SPIE_09_Gaussianization_presentation.pdf) ## Examples ```r 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) ``` ## See Also `GPCA`,`GPCA_iteration`,`inv_GPCA_iteration`,`inv_GPCA`,`GPCA-class` for 'GPCA' S3 class ## Author(s) Emanuele Cordano1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302 14.2222 5.70302 14.0299C5.70302 13.8576 5.69679 13.4019 5.69323 12.797C3.67661 13.235 3.25112 11.825 3.25112 11.825C2.92132 10.9874 2.44599 10.7644 2.44599 10.7644C1.78773 10.3149 2.49584 10.3238 2.49584 10.3238C3.22353 10.375 3.60629 11.0711 3.60629 11.0711C4.25298 12.1788 5.30335 11.8588 5.71638 11.6732C5.78225 11.205 5.96962 10.8854 6.17658 10.7043C4.56675 10.5209 2.87415 9.89918 2.

GPCA function