plot.bootpath {glmpath} | R Documentation |
This function takes a bootpath
object from
bootstrap.path
and generates the histograms or the pairwise
scatter plots of the bootstrap coefficients.
plot.bootpath(x, type=c("histogram","pairplot"), mfrow = NULL, mar = NULL, ...)
x |
a bootpath object from bootstrap.path.
|
type |
If type=histogram, the histograms of bootstrap coefficients
for individual features are generated. The red vertical bar
indicates the coefficient computed using the whole data. The thick
bar at zero indicates the frequency of the zero coefficients. If
type=pairplot, the pairwise scatter plots of the bootstrap
coefficients are generated. The red solid dot indicates the pair of
coefficients computed using the whole data. Default is
histogram.
|
mfrow |
determines the numbers of rows and columns of the histograms on a page. 2 rows are generated as a default. |
mar |
margin relative to the current font size |
... |
other options for the plot |
Fitting glmpath
or coxpath
gives a series of solution
sets with a varying size of the active set. Once we select an
appropriate value of the regularization parameter, and, thus a set of
coefficients, we may then validate the chosen coefficients through a
bootstrap analysis. plot.bootstrap
summarizes the bootstrap
results by generating the histograms or the pairwise scatter plots of
the bootstrap coefficients.
Mee Young Park and Trevor Hastie
Bradley Efron and Robert Tibshirani (1993) An Introduction to the Bootstrap CHAPMAN & HALL/CRC, Boca Raton.
Mee Young Park and Trevor Hastie (2007) L1 regularization path algorithm for generalized linear models. J. R. Statist. Soc. B, 69, 659-677.
bootstrap.path, coxpath, glmpath
data(heart.data) attach(heart.data) bootstrap.a <- bootstrap.path(x, y, B=100) plot(bootstrap.a) plot(bootstrap.a, type="pairplot") detach(heart.data)