theoryRowpenUpperBound {jointMeanCov} | R Documentation |
This function returns a theoretically-guided choice of the glasso penalty parameter, based on both the row and column covariance matrices.
theoryRowpenUpperBound(A, B, n1, n2)
A |
column covariance matrix. |
B |
row covariance matrix. |
n1 |
sample size of group one. |
n2 |
sample size of group two. |
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
# Define sample sizes
n1 <- 10
n2 <- 10
n <- n1 + n2
m <- 2e3
# Column covariance matrix (autoregressive of order 1)
A <- outer(1:n, 1:n, function(x, y) 0.2^abs(x - y))
# 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 <- theoryRowpenUpperBound(A, B, n1, n2)
print(rowpen)