hierNet.cv: Cross-validation function for hierNet

In pejovic/hierNet: A Lasso for Hierarchical Interactions

Description

Uses cross-validation to estimate the regularization parameter for hierNet

Usage

hierNet.cv(fit, x, y, nfolds=10,folds=NULL,trace=0)

Arguments

fit	Object returned from call to hierNet.path or hierNet.logistic.path. All parameter settings will be taken from this object.
x	A matrix of predictors, where the rows are the samples and the columns are the predictors
y	A vector of observations, where length(y) equals nrow(x)
nfolds	Number of cross-validation folds
folds	(Optional) user-supplied cross-validation folds. If provided, nfolds is ignored.
trace	Verbose output? 0=no, 1=yes
...	Additional arguments to be passed to hierNet.path or hierNet.logistic.path

Value

lamlist	Vector of lambda values tried
cv.err	Estimate of cross-validation error
cv.se	Estimated standard error of cross-validation estimate
lamhat	lambda value minimizing cv.err
lamhat.1se	largest lambda value with cv.err less than or equal to min(cv.err)+ SE
folds	Indices of folds used in cross-validation
yhat	n by nlam matrix of predicted values. Here, ith prediction is based on training on all folds that do not include the ith data point.
nonzero	Vector giving number of non-zero coefficients for each lambda value
call	The call to hierNet.cv

Author(s)

Jacob Bien and Robert Tibshirani

References

Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.

See Also

hierNet,hierNet.path, hierNet.logistic,hierNet.logistic.path

Examples

set.seed(12)
x=matrix(rnorm(100*10),ncol=10)
x=scale(x,TRUE,TRUE)
y=x[,1]+2*x[,2]+ x[,1]*x[,2]+3*rnorm(100)
fit=hierNet.path(x,y)
fitcv=hierNet.cv(fit,x,y)
print(fitcv)
plot(fitcv)


x=matrix(rnorm(100*10),ncol=10)
x=scale(x,TRUE,TRUE)
y=x[,1]+2*x[,2]+ x[,1]*x[,2]+3*rnorm(100)
y=1*(y>0)
fit=hierNet.logistic.path(x,y)
fitcv=hierNet.cv(fit,x,y)
print(fitcv)
plot(fitcv)

pejovic/hierNet documentation built on May 25, 2019, 12:45 a.m.