drawkpc {rdetools} | R Documentation |
The function plots the absolute values of the kernel pca coefficients. The estimated relevant dimension and the estimated noise level (if available) are also drawn. Optionally, it puts a rescaled version of the loo-cv-error/negative-log-likelihood into the plot.
drawkpc(model, err = TRUE, pointcol = "blue", rdcol = "red", noisecol = "black", errcol = "brown", ...)
model |
list of rde data returned by rde or selectmodel |
err |
leave this TRUE, if you want to have a rescaled version of the the loo-cv-error/negative-log-likelihood in the plot |
pointcol |
color of the kernel pca coefficients |
rdcol |
color of the relevant dimension line |
noisecol |
color of the noise level line |
errcol |
color of the the loo-cv-error/negative-log-likelihood |
... |
additional parameters to the plotting functions |
Jan Saputra Mueller
M. L. Braun, J. M. Buhmann, K. R. Mueller (2008) _On Relevant Dimensions in Kernel Feature Spaces_
rde
, selectmodel
, modelimage
, distimage
## draw kernel pca coefficients after calling rde d <- sincdata(100, 0.1) # generate sinc data K <- rbfkernel(d$X) r <- rde(K, d$y, est_noise = TRUE) drawkpc(r) ## draw kernel pca coefficients after calling selectmodel d <- sincdata(100, 0.1) # generate sinc data m <- selectmodel(d$X, d$y, est_noise = TRUE, sigma = logspace(-3, 3, 100)) drawkpc(m)