BigTSP (version 1.0)

cv.LDCA: Cross validation for LDCA

Description

Cross validation for LDCA

Usage

cv.LDCA(X, y, lambda = NULL, nfolds)

Arguments

X

input matrix, of dimension nobs x nvars; each row is an observation vector.

y

response variable.

lambda

user specified lambda sequence

nfolds

number of folds - default is 10.

Value

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

lambda

the values of lambda used in the fits.

cvm

The mean cross-validated error - a vector of length length(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.

name

a text string indicating type of measure (for plotting purposes).

glmnet.fit

a fitted glmnet object for the full data.

lambda.min

value of lambda that gives minimum cvm.

lambda.1se

largest value of lambda such that error is within 1 standard error of the minimum.

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, http://www.stanford.edu/~hastie/Papers/glmnet.pdf Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010 http://www.jstatsoft.org/v33/i01/ Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5) 1-13 http://www.jstatsoft.org/v39/i05/

See Also

print.cv.LDCA,predict.cv.LDCA,

Examples

library(glmnet)
x=matrix(rnorm(50*20),50,20)
y=rbinom(50,1,0.5)
cvfit=cv.LDCA(x,y,nfolds=5)
predict(cvfit,x[1:10,],s="lambda.min")