1d:T4a9,# This function makes an inverse Gaussianization procedure besad on PCA iteration ( see `inv_GPCA_iteration` ```r inv_GPCA(x = NULL, GPCA_param, type = 3, extremes = TRUE) ``` ## Arguments - `x`: gaussian random variable to transform - `GPCA_param`: `GPCA-class` S3 object returned by the function `GPCA` - `type`: see `normalizeGaussian_severalstations` - `extremes`: see `normalizeGaussian_severalstations` ## Returns the non-Gaussian random variable ## Note This function re-iterates the inverse of equation (1) of "PCA Gaussianization for One-Class Remote Sensing Image" by V. Laparra et al., [https://ieeexplore.ieee.org/document/5413808/](https://ieeexplore.ieee.org/document/5413808/) ## 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) df_out <- inv_GPCA(GPCA_param=GPCA,extremes=TRUE) dfn_out <- inv_GPCA(GPCA_param=GPCAn,extremes=TRUE) ``` ## See Also `GPCA`,`GPCA_iteration`,`inv_GPCA_iteration`,`inv_GPCA` ## 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 2.87415 7.12104C2.87415 6.32925 3.15677 5.68257 3.62053 5.17563C3.54576 4.99226 3.29697 4.25521 3.69174 3.25691C3.69174 3.25691 4.30015 3.06196 5.68522 3.99973C6.26337 3.83906 6.8838 3.75895 7.50022 3.75583C8.1162 3.75895 8.73619 3.83906 9.31523 3.99973C10.6994 3.06196 11.3069 3.25691 11.3069 3.25691C11.7026 4.25521 11.4538 4.99226

inv_GPCA function