Description Usage Arguments Details Value Author(s) References See Also Examples
produces a plot of the svm solution along the path, and optinally indicates support points
1 2 |
x |
the |
step |
which step to plot; default is the last step. Use
|
Size |
If the solution is non-linear, this is the gridsize for |
elbow.show |
Should the points on the elbow be indicated |
support.show |
Should the support points be indicated |
... |
additional arguments to plot, allowing one to change, for example, "main", "xlab" etc |
A two-dimensional plot is produced of the SVM solution. Makes sense only if X is two-dimensional. If not, the first two dimensions will be used
A list is returned silently, with the ingredients of the plot
Trevor Hastie
The paper http://www-stat.stanford.edu/~hastie/Papers/svmpath.pdf, as well as the talk http://www-stat.stanford.edu/~hastie/TALKS/svmpathtalk.pdf.
coef.svmpath, svmpath, predict.svmpath, print.svmpath,summary.svmpath
1 2 3 4 5 |
Loaded svmpath 0.955
1: Obs 11 ->E lambda = 3.851000 Sum Eps = 8.7 Elbow = 2 Error = 2
1: Obs 6 ->E lambda = 3.851000 Sum Eps = 8.7 Elbow = 2 Error = 2
2: Obs 11 E->R lambda = 1.489298 Sum Eps = 8.63 Elbow = 0 Error = 3
2: Obs 6 E->R lambda = 1.489298 Sum Eps = 8.63 Elbow = 0 Error = 3
3: Obs 12 ->E lambda = 1.047224 Sum Eps = 8.06 Elbow = 2 Error = 3
3: Obs 3 ->E lambda = 1.047224 Sum Eps = 8.06 Elbow = 2 Error = 3
4: Obs 1 L->E lambda = 0.907920 Sum Eps = 8 Elbow = 3 Error = 3
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