jmlr06data {qp} | R Documentation |
Synthetic data generated from two graphs with 150 vertices, $G_1$ and $G_2$. In $G_1$ the boundary of every vertex is at most 5, while in $G_2$ the boundary of every vertext is at most 20
data(jmlr06data)
IC.bd5: | inverse correlation matrix encoding the independence structure of $G_1$ |
IC.bd20: | inverse correlation matrix encoding the independence structure of $G_2$ |
S.bd5.N20: | sample covariance matrix from a sample of size 20 drawn from a normal |
distribution with mean 0 and inverse correlation matrix IC.bd5 | |
S.bd5.N50: | sample covariance matrix from a sample of size 50 drawn from a normal |
distribution with mean 0 and inverse correlation matrix IC.bd5 | |
S.bd5.N150: | sample covariance matrix from a sample of size 150 drawn from a normal |
distribution with mean 0 and inverse correlation matrix IC.bd5 | |
S.bd20.N20: | sample covariance matrix from a sample of size 20 drawn from a normal |
distribution with mean 0 and inverse correlation matrix IC.bd20 | |
S.bd20.N50: | sample covariance matrix from a sample of size 50 drawn from a normal |
distribution with mean 0 and inverse correlation matrix IC.bd20 | |
S.bd20.N150: | sample covariance matrix from a sample of size 150 drawn from a normal |
distribution with mean 0 and inverse correlation matrix IC.bd20 | |
qp.out.bd5.N20.q10: | output from qp.search applied to S.bd5.N20 with q=10 and T=500 |
qp.out.bd20.N20.q10: | output from qp.search applied to S.bd20.N20 with q=10 and T=500 |
Castelo, R. and Roverato, A. (2006). A robust procedure for Gaussian graphical model search from microarray data with p larger than n, J. Mach. Learn. Res., 7:2621-2650