flare-internal {flare}

Internal flare functions

Description

Internal flare functions

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

Sigma

Covariance matrix.

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).

d

Dimension of data.

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.

prec

Stopping criterion.

max.ite

Maximal value of iterations.

data

n by d data matrix.

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.

Details

These are not intended for use by users.

Author(s)

Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu
Maintainer: Xingguo Li <[e-mail]>

See Also

sugm, slim and flare-package.