Contributions of the variables to the Principal Components ("loadings")
Source:R/kernel_pca.R
kPCA_imp.Rd`kPCA_imp()` performs a PCA and a kernel PCA simultaneously and returns the contributions of the variables to the Principal Components (sometimes, these contributions are called "loadings") in Feature Space. Optionally, it can also return the samples' projection (cropped to the relevant PCs) and the values used to centering the variables in Feature Space. It does not return any plot, nor it projects test data. To do so, please use `kPCA()`.
Arguments
- DATA
A matrix or data.frame (NOT a kernel matrix) containing the data in feature space. Please note that nrow(DATA) should be higher than ncol(DATA). If the Linear kernel is used, this feature space is simply the original space.
- center
A logical value. If TRUE, the variables are zero-centered. (Defaults: TRUE).
- projected
(optional) If desired, the PCA projection (generated, for example, by `kPCA()`) can be included. If DATA is big (especially in the number of rows) this may save some computation time.
- secure
(optional) If TRUE, it tests the quality of the loadings This may be slow. (Defaults: FALSE).
Value
A list with three objects:
* The PCA projection (class "matrix") using only the relevant Principal Components.
* The loadings.
* The values used to center each variable in Feature Space.
Details
This function may be not valid for all kernels. Do not use it with the RBF, Laplacian, Bray-Curtis, Jaccard/Ruzicka, or Kendall's tau kernels unless you know exactly what you are doing.
Examples
dat <- matrix(rnorm(150),ncol=30,nrow=50)
contributions <- kPCA_imp(dat)
#> Do not use this function if the PCA was created with the RBF,
#> Laplacian, Bray-Curtis, Jaccard/Ruzicka, or Kendall's tau kernels
contributions$loadings[c("PC1","PC2"),1:5]
#> [,1] [,2] [,3] [,4] [,5]
#> PC1 -0.299579952 0.02798332 -0.09730769 -0.299579952 0.02798332
#> PC2 -0.008926763 0.29542758 0.11244047 -0.008926763 0.29542758