muscor.model {muscor} | R Documentation |
Create a muscor model. To be used for muscor().
muscor.model (coefficients=NULL, loss.type=c("Least.Squares","Logistic.Regression", "Modified.LS","Modified.Huber"), lambda.ridge=0, lambda.sparse=0, sparse.type=c("capped.L1","Lp"), sparse.param=0, intercept=FALSE)
coefficients |
The computed coefficients. If intercept=TRUE, it has d+1 components, with the last component being the intercept parameter. Otherwise, it has d components. Default is NULL. |
loss.type |
One of "Least.Squares","Logistic.Regression", "Modified.LS","Modified.Huber". The names can be abbreviated to any unique substring. Least.Squares is for regression with real-valued y. The other loss functions are for binary classification, assuming y to be {+1,-1} valued, and described in [Tong Zhang (2004)]. Default is "Least.Squares". |
lambda.ridge |
Ridge (L2) regularization parameter. Default is zero. |
lambda.sparse |
Sparse regularization parameter: it can be a vector of size "stages" to specific different regularization strength for different stages. Default is zero. |
sparse.type |
One of "capped.L1" or "Lp". Default is "capped.L1". |
sparse.param |
If sparse.type="Lp", R(w)=|w|_p^p, and sparse.param is the p in (0,1]. If sparse.type=capped.L1, R(w)=sum_j max(|w_j|,g); sparse.param=g if it has positive value, and g is choen such as no more than |sparse.param| number of |w| is larger than g if it has value <= 0. Default is 0. |
intercept |
If TRUE, an intercept is included in the model (and not penalized); otherwise no intercept is included. Default is FALSE. |
The Multi-stage Convex Relaxation approach is described in [Tong Zhang (2008)]. It relaxes the non-convex problem into L1 regularization problems in stages, and each L1 regularization problem is solved using the function opt.L1().
A muscor model is returned. It is a list containing the same components as the arguments.
Tong Zhang
Tong Zhang (2004) "Statistical Behavior and Consistency of Classification Methods based on Convex Risk Minimization", Annals of Statistics, 32:56–85, 2004.
Tong Zhang (2008) "Multi-stage Convex Relaxation for Learning with Sparse Regularization", NIPS'08.
muscor predict.muscor and opt.L1