Internal camel functions

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Description

Internal camel functions

Usage

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tiger.likelihood(Sigma, Omega)
tiger.tracel2(Sigma, Omega)
camel.tiger.cv(obj, loss=c("likelihood", "tracel2"), fold=5)
part.cv(n, fold)
camel.tiger.clime.mfista(Sigma, d, maxdf, mu, lambda, shrink, prec, max.ite)
camel.tiger.slasso.mfista(data, n, d, maxdf, mu, lambda, shrink, prec, max.ite)
camel.slim.lad.mfista(Y, X, lambda, nlambda, n, d, maxdf, mu, max.ite, prec, 
                      intercept, verbose)
camel.slim.sqrt.mfista(Y, X, lambda, nlambda, n, d, maxdf, mu, max.ite, prec, 
                       intercept, verbose)
camel.slim.dantzig.mfista(Y, X, lambda, nlambda, n, d, maxdf, mu, max.ite, prec, 
                          intercept, verbose)
camel.cmr.mfista(Y, X, lambda, nlambda, n, d, m, mu, max.ite, prec)

Arguments

Sigma

Covariance matrix.

Omega

Inverse covariance matrix.

obj

An object with S3 class returned from "tiger".

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.

m

Columns of parameters in multivariate regression.

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.

mu

The smooth surrogate parameter.

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.

intercept

Whether the intercept is included in the model.

verbose

Tracing information printing is disabled if verbose = FALSE.

Details

These are not intended for use by users.

Author(s)

Xingguo Li, Tuo Zhao, and Han Liu
Maintainer: Xingguo Li <xingguo.leo@gmail.com>

See Also

camel.tiger, camel.slim, camel.cmr and camel-package.