Description Usage Arguments Value References Examples

View source: R/theoryRowpenUpperBoundDiagA.R

This function returns a theoretically-guided choice of the glasso penalty parameter, treating the column correlation matrix as the identity.

1 | ```
theoryRowpenUpperBoundDiagA(B, n1, n2, m)
``` |

`B` |
row covariance matrix. |

`n1` |
sample size of group one. |

`n2` |
sample size of group two. |

`m` |
number of columns of the data matrix (where the data matrix is of size n by m, with n = n1 + n2). |

Returns a theoretically guided choice of the glasso penalty parameter.

Joint mean and covariance estimation with unreplicated matrix-variate data Michael Hornstein, Roger Fan, Kerby Shedden, Shuheng Zhou (2018). Joint mean and covariance estimation with unreplicated matrix-variate data. Journal of the American Statistical Association

1 2 3 4 5 6 7 8 9 10 | ```
# Define sample sizes
n1 <- 10
n2 <- 10
n <- n1 + n2
m <- 2e3
# Row covariance matrix (autoregressive of order 1)
B <- outer(1:n, 1:n, function(x, y) 0.8^abs(x - y))
# Calculate theoretically guided Gemini penalty.
rowpen <- theoryRowpenUpperBoundDiagA(B, n1, n2, m)
print(rowpen)
``` |

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