GeminiBPath: Estimate Row-Row Covariance Using Gemini for a Sequence of...

In jointMeanCov: Joint Mean and Covariance Estimation for Matrix-Variate Data

Description

GeminiBPath estimates the row-row covariance, inverse covariance, correlation, and inverse correlation matrices using Gemini with a sequence of penalty parameters. For identifiability, the covariance factors A and B are scaled so that A has trace m, where m is the number of columns of X, A is the column-column covariance matrix, and B is the row-row covariance matrix.

Usage

GeminiBPath(X, rowpen.list, penalize.diagonal = FALSE)

Arguments

X	Data matrix, of dimensions n by m.
rowpen.list	Vector of penalty parameters, should be increasing (analogous to the glassopath function of the glasso package).
penalize.diagonal	Logical indicating whether to penalize the off-diagonal entries of the correlation matrix. Default is FALSE.

Value

corr.B.hat	array of estimated correlation matrices, of dimension (nrow(X), nrow(X), length(rowpen.list)).
corr.B.hat.inv	array of estimated inverse correlation matrices, of dimension (nrow(X), nrow(X), length(rowpen.list)).
B.hat	array of estimated covariance matrices, of dimension (nrow(X), nrow(X), length(rowpen.list)).
B.hat.inv	array of estimated inverse covariance matrices, of dimension (nrow(X), nrow(X), length(rowpen.list)).

Examples

# Generate a data matrix.
n1 <- 5
n2 <- 5
n <- n1 + n2
m <- 20
X <- matrix(rnorm(n * m), nrow=n, ncol=m)

# Apply GeminiBPath for a sequence of penalty parameters.
rowpen.list <- sqrt(log(m) / n) * c(1, 0.5, 0.1)
out <- GeminiBPath(X, rowpen.list, penalize.diagonal=FALSE)

# Display the estimated correlation matrix corresponding
# to penalty 0.1, rounded to two decimal places.
print(round(out$corr.B.hat[, , 3], 2))

jointMeanCov documentation built on May 6, 2019, 1:09 a.m.