Values of the penalty parameter lambda to be tried. For speed, it's advised that a decreasing vector be used. If NULL, a log grid used, using liso.maxlamb to calculate the maximum.
trace
If TRUE, print diagnostic information as calculation is done.
plot.it
If TRUE, plot a graph of CV error against lambda with plotCV.
weights
Observation weights. Should be a vector of length equal to the number of observations.
weightedcv
If TRUE, use observation weights when averaging CV error across folds.
huber
If less than Inf, huberisation parameter for huberised liso. (Experimental)
covweights
Covariate weights. Should be a vector of length equal to the number of covariates.
gridsize
Size of logarithmic grid of lambda values, if lambda is unspecified.
gridmin
Minimum of logarithmic grid of lambda values, if lambda is unspecified. Considered as a proportion of the maximum value of lambda.
For plotCV:
cv.object
Object to be plotted.
For both:
se
If TRUE, add error bars to CV plot.
...
Additional arguments to be passed to liso.backfit or plot
Value
cv.liso creates a list of 5 components:
lambda
Lambda values used.
cv
Mean or weighted mean CV for each lambda.
cv.error
Sqrt of MLE estimated variance of CV for each lambda.
residmat
Full length(lambda) x K matrix of CV errors.
optimlam
Lambda value that minimises CV error
Author(s)
Zhou Fang
References
Zhou Fang and Nicolai Meinshausen (2009),
Liso for High Dimensional Additive Isotonic Regression, available at
http://blah.com
See Also
liso.backfit
Examples
## Use the method on a simulated data set
set.seed(79)
n <- 100; p <- 50
## Simulate design matrix and response
x <- matrix(runif(n * p, min = -2.5, max = 2.5), nrow = n, ncol = p)
y <- scale(3 * (x[,1]> 0), scale=FALSE) + x[,2]^3 + rnorm(n)
## Do CV
CVobj <- cv.liso(x,y, K=10, plot.it=TRUE)
## Do the actual fit
fitobj <- liso.backfit(x,y,CVobj$optimlam)
plot(fitobj)