# Explicit Eigenfunction Approximation from Eigendecomposition of Radial Basis Kernel Matrix

I am currently studying Kernel Ridge Regression. Specifically, I am considering the radial basis function kernel. Throughout literature I am seeing plots of the eigenfunctions that are the approximations of the eigenfunctions of the Kernel Operator. Below is a picture of the first 16 eigenfunctions from (Tibshirani et al 2008). I would like to know how to find an explicit expression for these functions. Perhaps there is a function in the R package, kernlab.

How can I represent the eigenvectors of kernel matrix on $\mathbb{R}$? Also, are these scaled eigenfunctions that are the feature space essentially the first 16 scores of Kernel Principal Component Analysis using the RBF?