Description Usage Arguments Details Value Author(s) References See Also Examples
Dose k-fold cross-validation for QICD, produces
a plot and returns an appropriate tuning
parameter lambda
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y |
response |
x |
|
beta |
|
tau |
|
lambda |
a user supplied |
nfolds |
number of folds - default is 10. |
a |
|
funname |
|
intercept |
|
thresh |
|
maxin |
|
maxout |
|
mc.cores |
The number of cores to use for parallel computing, i.e. at most how many child processes will be run simultaneously. The option is initialized from environment variable MC_CORES if set. Must be at least one, and parallelization requires at least two cores. |
plot.off |
a logical value to control if a plot of prediction error vs. |
... |
other argument that can be passed to |
The function run QICD
nfolds
times. For each specific lambda
, the average test prediction error will be produced for comparison. Claim that QICD.cv
does NOT search for values of a
.
an object of class "cv.qicd" is returned, which is a list with the components of the cross-validation fit.
lambda |
the values of |
cvm |
The mean cross-validated error-a vetor of length nlambda as in |
cvsd |
estimate of standard error of cvm. |
cvup |
upper curve = cvm+cvsd. |
cvlo |
upper curve = cvm-cvsd. |
nzero |
number of non-zero coefficients at each |
lambda.min |
value of |
lambda.1se |
largest value of |
Bo Peng
Peng,B and Wang,L. (2015)An Iterative Coordinate Descent Algorithm for High-dimensional Nonconvex Penalized Quantile Regression. Journal of Computational and Graphical Statistics http://amstat.tandfonline.com/doi/abs/10.1080/10618600.2014.913516 doi: 10.1080/10618600.2014.913516
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