Usage
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
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).
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.
max.ite
Maximal value of iterations.
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.