drawkpc: Draw kernel pca coefficients

Description Usage Arguments Author(s) References See Also Examples

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

1
2
3
4
5
6
7
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

Author(s)

Jan Saputra Mueller

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
## 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)

Example output



rdetools documentation built on May 29, 2017, 8:50 p.m.