cv.apple: cross validation for apple path

In apple: Approximate Path for Penalized Likelihood Estimators

Description

Does K-fold cross validation for apple.

Usage

cv.apple(X, y, family="binomial", penalty="LASSO", gamma,  K = 10, 
alpha=0, seed = 1, cha.poi = 1, eps = 1e-15, lambda.min.ratio, 
max.iter = 100, max.num, n.lambda = 100)

Arguments

X	input matrix, of dimension nobs x nvars; each row is an observation vector.
y	response variable, of dimension nobs x 1. non-negative counts for family="poisson", binary for family="binomial".
family	response type.
penalty	LASSO and MCP are provided.
gamma	the MCP concavity parameter.
K	number of folds used in cross validation. The default it 10.
alpha	weight used in the cross validation cost function, with Q (λ) =α Dev(λ)+ (1-α) s(λ) \log n/n.
seed	random seed used to sample the training sets and test sets
cha.poi	the value used to change from Newton Raphson correction to Coordinate Descent correction, which is the α in the following inequality, k> α√{n}, where k is the size of current active set. when this inequality holds, the correction method changes from Newton Raphson to Coordinate Descent.
eps	the precision used to test the convergence.
lambda.min.ratio	optional input. smallest value for lambda, as a fraction of max.lam, the (data derived) entry value. the default depends on the sample size n relative to the number of variables p. if n > p, the default is 0.0001. otherwise, the default is 0.01.
max.iter	maximum number of iteration in the computation.
max.num	optional input. maximum number of nonzero coefficients.
n.lambda	the number of lambda values.

Value

cv	list of cross validation loss
lambda	list of lambda
a0	the list of intercept
beta	the list of coefficients
cv.loc	location of cv selected solution in the path
ebic.loc	the location of the EBIC selected solution in the path
cv.beta	cross validation selected beta
ebic.beta	ebic selected beta
cv.a0	cv selected intercept
ebic.a0	ebic selected intercept

Author(s)

Yi Yu and Yang Feng

References

Yi Yu and Yang Feng, APPLE: Approximate Path for Penalized Likelihood Estimator, manuscript.

See Also

plot.apple, apple and predict.apple

Examples

p=10
n=200
d=5
coefs=c(3,1.5,0,0,2)
intercept=0
beta=rep(0,p)
beta[1:d]=coefs
X=matrix(rnorm(p*n), nrow=n)
mu=1/(1+exp(-X %*% beta-intercept))
y=rbinom(n,1,mu)


fit.cv=cv.apple(X, y, family="binomial", alpha=0.25, K=5)


plot(fit.cv)

apple documentation built on May 2, 2019, 3:23 a.m.