Sparse Covariance Selection by glasso with EM

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Description

A function to achieve covariance selection by glasso with EM, missing data is allowed

Usage

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glasso.miss(Y, emIter, rho, ...)

Arguments

Y

numeric matrix. Columns are for variables and rows are for samples.

emIter

numeric value. The maximum number of iteration for EM

rho

The l1 norm tuning parameter.

...

additional control parameter passed to glasso

Value

a list of following comopnents.

Y.imputed

The sample matrix Y with missing data imputed by EM

bic

the BIC(Bayesian Information Criterion) of current fit.

...

other values returned by glasso

Author(s)

Shiyuan He

References

He, S.Y., Wang, X., and Yuan, W.(2012), Discovering Co-movement Structure of Chinese Stock Market by SPACE method with EM

Jerome Friedman, Trevor Hastie and Robert Tibshirani (2007). Sparse inverse covariance estimation with the lasso. Biostatistics 2007.

Meinshausen, N. and Buhlmann, P.(2006) High dimensional graphs and variable selection with the lasso. Annals of Statistics,34, p1436-1462.

Daniela Witten, Jerome Friedman, and Noah Simon (2011). New insights and faster computations for the graphical lasso. To appear in Journal of Computational and Graphical Statistics.

Examples

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data(finStocksCn)  ##data of finance sector of Chinese Stock Market, from January 4th, 2011 to July 26th, 2011
finStocksCn$names ##stock names
y.m<-scale(finStocksCn$returns)
n=nrow(y.m)
p=ncol(y.m)
res=glasso.miss(y.m,rho=0.030,emIter=25,penalize.diagonal=FALSE)
res$bic  ##bic returned