Description Usage Arguments Value Author(s) References See Also Examples
Does K-fold cross validation for apple.
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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 |
response type. |
penalty |
|
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 |
max.iter |
maximum number of iteration in the computation. |
max.num |
optional input. maximum number of nonzero coefficients. |
n.lambda |
the number of |
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 |
Yi Yu and Yang Feng
Yi Yu and Yang Feng, APPLE: Approximate Path for Penalized Likelihood Estimator, manuscript.
plot.apple
, apple
and predict.apple
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