cv.sparsenet: Cross-validation for sparsenet

In sparsenet: Fit Sparse Linear Regression Models via Nonconvex Optimization

Cross-validation for sparsenet

Description

Does k-fold cross-validation for sparsenet, produces a plot, and returns values for gamma, lambda

Usage

cv.sparsenet(x, y, weights, type.measure = c("mse", "mae"), ...,nfolds = 10,
       foldid, trace.it=FALSE)

Arguments

x	x matrix as in sparsenet.
y	response y as in sparsenet.
weights	Observation weights; defaults to 1 per observation
type.measure	loss to use for cross-validation. Currently two options: squared-error (type.measure="mse") or mean-absolute error ( type.measure="mae" )
...	Other arguments that can be passed to sparsenet.
nfolds	number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3
foldid	an optional vector of values between 1 and nfold identifying whhat fold each observation is in. If supplied, nfold can be missing.
trace.it	If TRUE, then we get a printout that shows the progress

Details

The function runs sparsenet nfolds+1 times; the first to get the lambda sequence, and then the remainder to compute the fit with each of the folds omitted. The error is accumulated, and the average error and standard deviation over the folds is computed.

Value

an object of class "cv.sparsenet" is returned, which is a list with the ingredients of the cross-validation fit.

lambda	the values of lambda used in the fits. This is an nlambda x ngamma matrix
cvm	The mean cross-validated error - a matrix shaped like lambda
cvsd	estimate of standard error of cvm.
cvup	upper curve = cvm+cvsd.
cvlo	lower curve = cvm-cvsd.
nzero	number of non-zero coefficients at each lambda, gamma pair.
name	a text string indicating type of measure (for plotting purposes).
sparsenet.fit	a fitted sparsenet object for the full data.
call	The call that produced this object
parms.min	values of gamma, lambda that gives minimum cvm.
which.min	indices for the above
lambda.1se	gamma, lambda of smallest model (df) such that error is within 1 standard error of the minimum.
which.1se	indices of the above

Author(s)

Rahul Mazumder, Jerome Friedman and Trevor Hastie

Maintainer: Trevor Hastie <[e-mail]>

References

Mazumder, Rahul, Friedman, Jerome and Hastie, Trevor (2011) SparseNet: Coordinate Descent with Nonconvex Penalties. JASA, Vol 106(495), 1125-38, https://hastie.su.domains/public/Papers/Sparsenet/Mazumder-SparseNetCoordinateDescent-2011.pdf

See Also

glmnet package, predict, coef, print and plot methods, and the sparsenet function.

Examples

train.data=gendata(100,1000,nonzero=30,rho=0.3,snr=3)
fit=sparsenet(train.data$x,train.data$y)
par(mfrow=c(3,3))
plot(fit)
par(mfrow=c(1,1))
fitcv=cv.sparsenet(train.data$x,train.data$y,trace.it=TRUE)
plot(fitcv)

sparsenet documentation built on Aug. 22, 2023, 9:11 a.m.