plot.l2boost {l2boost}

Plotting for l2boost objects.

Description

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.

Usage

## S3 method for class 'l2boost'
plot(
  x,
  type = c("rho", "coef"),
  standardize = TRUE,
  active.set = NULL,
  xvar = c("step", "norm"),
  xlab = NULL,
  ylab = NULL,
  trim = TRUE,
  clip = NULL,
  col = NULL,
  ylim = NULL,
  xlim = NULL,
  ...
)

Arguments

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

Details

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.

Value

NULL

See Also

l2boost, print.l2boost, predict.l2boost methods of l2boost and cv.l2boost

Examples

#--------------------------------------------------------------------------
# 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)