Last data update: 2014.03.03
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Classification
Cran Task View
BiocViews
SMO (statistical methods ontology)
Results 1 - 9 of 9 found.[1]
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predict.sparsenet
(Package: sparsenet ) :
make predictions from a "sparsenet" object.
"sparsenet" object.
● Data Source:
CranContrib
cv.sparsenet
(Package: sparsenet ) :
Cross-validation for sparsenet
gamma, lambda
● Data Source:
CranContrib
This function generates x/y data for testing sparsenet and glmnet
● Data Source:
CranContrib
Sparsenet uses coordinate descent on the MC+ nonconvex penalty family, and fits a surface of solutions over the two-dimensional parameter space. This penalty family is indexed by an overall strength paramter lambda (like lasso), and a convexity parameter gamma. Gamma = infinity corresponds to the lasso, and gamma = 1 best subset.
● Data Source:
CranContrib
Internal sparsenet functions
● Data Source:
CranContrib
plot.sparsenet
(Package: sparsenet ) :
plot coefficients from a "sparsenet" object
"sparsenet" object.
● Data Source:
CranContrib
plot.cv.sparsenet
(Package: sparsenet ) :
plot the cross-validation curves produced by cv.sparsenet
gamma in one figure, as a function of the lambda values used.
● Data Source:
CranContrib
predict.cv.sparsenet
(Package: sparsenet ) :
make predictions from a "cv.sparsenet" object.
"sparsenet.fit" object, and the optimal value chosen for lambda.
● Data Source:
CranContrib
Sparsenet uses coordinate descent on the MC+ nonconvex penalty family, and fits a surface of solutions over the two-dimensional parameter space.
● Data Source:
CranContrib
providing 
.