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rdetools (version 1.0)

drawkpc: Draw kernel pca coefficients

Description

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.

Usage

drawkpc(model, err = TRUE,
	pointcol = "blue",
	rdcol = "red",
	noisecol = "black",
	errcol = "brown",
	...)

Arguments

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

References

M. L. Braun, J. M. Buhmann, K. R. Mueller (2008) \_On Relevant Dimensions in Kernel Feature Spaces\_

See Also

rde, selectmodel, modelimage, distimage

Examples

Run this code
## 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)

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