Description Usage Arguments Details Value See Also Examples
plotting methods for l2boost
objects (l2boost
and cv.l2boost
).
By default, plotting an l2boost
object produces a gradient-correlation vs iteration steps (m) plot.
Plotting a cv.l2boost
object produces a cross-validation error plot, and prints the minimal CV MSE value
and optimal step opt.step to the R console.
Many generic arguments to plot
are passed through the plot.l2boost
function.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
x |
l2boost or cv.l2boost object |
type |
which type of plot. rho plots gradient-correlation, coef regression (beta) coefficients vs the step number m along the x-axis |
standardize |
Should we plot standardized gradient-correlation (default: TRUE) |
active.set |
Vector of indices of the coordinates for highlighting with color=col (default: NULL shows all active coordinates) |
xvar |
what measure do we plot on the x-axis? step plots the step m, norm plots the normalized distance (1-nu)^(m-1) |
xlab |
specific x-axis label (NULL results in default value depending on xvar) |
ylab |
specific y-axis label (NULL results in default value depending on type) |
trim |
(default: TRUE) |
clip |
Do we want to c |
col |
Color to highlight active.set coordinates (NULL indicates default all active set at step M in blue, changes to red after selection |
ylim |
Control plotted y-values (default: NULL for auto range) |
xlim |
Control plotted x-values (default: NULL for auto domain ) |
... |
other arguments passed to plot functions |
Gradient-correlation plots are created by tracing out the boosting coefficient (rho) for each candidate direction. The coefficient and gradient-correlation are equivalent under standard scaling (zero intercept with design matrix columns scaled to have mean=0 and variance=1).
Unless explicitly set using col argument, the plot function colors the gradient-correlation paths along each direction by the following criteria:
Red: indicates the coordinate direction has been selected in the boosting path at some step <= m.
Blue: indicates the coordinate will be selected within the specified number of steps M (and switch to red upon selection).
Grey: indicates coordinates have not and will not be selected by the algorithm over all iterations.
The colors are set using the l.crit return value from the l2boost
object.
NULL
l2boost
, print.l2boost
, predict.l2boost
methods of l2boost
and cv.l2boost
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | #--------------------------------------------------------------------------
# Example: Diabetes
#
# See Efron B., Hastie T., Johnstone I., and Tibshirani R.
# Least angle regression. Ann. Statist., 32:407-499, 2004.
data(diabetes, package = "l2boost")
l2.object <- l2boost(diabetes$x,diabetes$y, M=1000, nu=.01)
# Plot the gradient-correlation, and regression beta coefficients as a function of
# boosting steps m
par(mfrow=c(2,2))
plot(l2.object)
abline(v=500, lty=2, col="grey")
plot(l2.object, type="coef")
abline(v=500, lty=2, col="grey")
# limit the plot to only the first 500 steps of the algorithm
# (grey vertical line in previous plots).
plot(l2.object, xlim=c(0,500))
plot(l2.object, type="coef", xlim=c(0,500))
## Not run:
#--------------------------------------------------------------------------
# Example: Plotting cross-validation objects
dta <- elasticNetSim(n=100)
# Set the boosting parameters
Mtarget = 1000
nuTarget = 1.e-2
cv.l2 <- cv.l2boost(dta$x,dta$y,M=Mtarget, nu=nuTarget, lambda=NULL)
# Show the CV MSE plot, with a marker at the "optimal iteration"
plot(cv.l2)
abline(v=cv.l2$opt.step, lty=2, col="grey")
# Show the l2boost object plots.
plot(cv.l2$fit)
abline(v=cv.l2$opt.step, lty=2, col="grey")
plot(cv.l2$fit, type="coef")
abline(v=cv.l2$opt.step, lty=2, col="grey")
# Create a color vector of length p=40 (from elasticNetSim defaults)
clr <- rep("black", 40)
# Set coordinates in the boosting path to color red.
clr[unique(cv.l2$fit$l.crit)] = "red"
# Show the "optimal" coefficient values,
# red points are selected in boosting algorithm.
plot(coef(cv.l2$fit, m=cv.l2$opt.step), col=clr, ylab=expression(beta))
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.