Usage
sugm.likelihood(Sigma, Omega)
sugm.tracel2(Sigma, Omega)
sugm.cv(obj, loss=c("likelihood", "tracel2"), fold=5)
part.cv(n, fold)
sugm.clime.ladm.scr(Sigma, lambda, nlambda, n, d, maxdf, rho, shrink, prec, max.ite, verbose)
sugm.tiger.ladm.scr(data, n, d, maxdf, rho, lambda, shrink, prec, max.ite, verbose)
slim.lad.ladm.scr.btr(Y, X, lambda, nlambda, n, d, maxdf, rho, max.ite, prec, intercept, verbose)
slim.sqrt.ladm.scr(Y, X, lambda, nlambda, n, d, maxdf, rho, max.ite, prec, intercept, verbose)
slim.dantzig.ladm.scr(Y, X, lambda, nlambda, n, d, maxdf, rho, max.ite, prec, intercept, verbose)
slim.lq.ladm.scr.btr(Y, X, q, lambda, nlambda, n, d, maxdf, rho, max.ite, prec, intercept, verbose)
slim.lasso.ladm.scr(Y, X, lambda, nlambda, n, d, maxdf, max.ite, prec, intercept, verbose)
Arguments
Omega
Inverse covariance matrix.
obj
An object with S3 class returned from "sugm".
loss
Type of loss function for cross validation.
fold
The number of fold for cross validatio.
n
The number of observations (sample size).
maxdf
Maximal degree of freedom.
lambda
Grid of non-negative values for the regularization parameter lambda.
nlambda
The number of the regularization parameter lambda.
shrink
Shrinkage of regularization parameter based on precision of estimation.
rho
Value of augmented Lagrangian multipiler.
max.ite
Maximal value of iterations.
Y
Dependent variables in linear regression.
X
Design matrix in linear regression.
q
The vector norm used for the loss term.
intercept
The indicator of whether including intercepts specifically.
verbose
Tracing information printing is disabled if verbose = FALSE. The default value is TRUE.