plot.l2boost: Plotting for 'l2boost' objects.

Description Usage Arguments Details Value See Also Examples

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

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## 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:

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

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

ehrlinger/l2boost documentation built on May 16, 2019, 1:20 a.m.