Row-sparse reduced-rank regression tuned by cross validation
Row-sparse reduced-rank regression tuned by cross validation
cv.srrr(Y, X, nrank = 1, method = c("glasso", "adglasso"), nfold = 5,
norder = NULL, A0 = NULL, V0 = NULL, modstr = list(),
control = list())Y |
response matrix |
X |
covariate matrix |
nrank |
prespecified rank |
method |
group lasso or adaptive group lasso |
nfold |
fold number |
norder |
for constructing the folds |
A0 |
initial value |
V0 |
initial value |
modstr |
a list of model parameters controlling the model fitting |
control |
a list of parameters for controlling the fitting process |
Model parameters controlling the model fitting can be specified through
argument modstr. The available elements include
lamA: tuning parameter sequence.
nlam: number of tuning parameters; no effect if lamA is
specified.
minLambda: minimum lambda value, no effect if lamA is
specified.
maxLambda: maxmum lambda value, no effect if lamA is specified.
WA: adaptive weights. If NULL, the weights are constructed from RRR.
wgamma: power parameter for constructing adaptive weights.
Similarly, the computational parameters controlling optimization can be
specified through argument control. The available elements include
epsilon: epsilonergence tolerance.
maxit: maximum number of iterations.
inner.eps: used in inner loop.
inner.maxit: used in inner loop.
A list of fitting results
Chen, L. and Huang, J.Z. (2012) Sparse reduced-rank regression for simultaneous dimension reduction and variable selection. Journal of the American Statistical Association. 107:500, 1533–1545.
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