summary.bspcov {bspcov}

Summary of Posterior Distribution

Description

Provides the summary statistics for posterior samples of covariance matrix.

Usage

## S3 method for class 'bspcov'
summary(object, cols, rows, ...)

Arguments

object

an object from bandPPP, bmspcov, sbmspcov, and thresPPP.

cols

a scalar or a vector including specific column indices.

rows

a scalar or a vector including specific row indices greater than or equal to columns indices.

...

additional arguments for the summary function.

Value

summary

a table of summary statistics including empirical mean, standard deviation, and quantiles for posterior samples

Note

If both cols and rows are vectors, they must have the same length.

Author(s)

Seongil Jo

Examples

set.seed(1)
n <- 20
p <- 5

# generate a sparse covariance matrix:
True.Sigma <- matrix(0, nrow = p, ncol = p)
diag(True.Sigma) <- 1
Values <- -runif(n = p*(p-1)/2, min = 0.2, max = 0.8)
nonzeroIND <- which(rbinom(n=p*(p-1)/2,1,prob=1/p)==1)
zeroIND = (1:(p*(p-1)/2))[-nonzeroIND]
Values[zeroIND] <- 0
True.Sigma[lower.tri(True.Sigma)] <- Values
True.Sigma[upper.tri(True.Sigma)] <- t(True.Sigma)[upper.tri(True.Sigma)]
if(min(eigen(True.Sigma)$values) <= 0){
  delta <- -min(eigen(True.Sigma)$values) + 1.0e-5
  True.Sigma <- True.Sigma + delta*diag(p)
}

# generate a data
X <- MASS::mvrnorm(n = n, mu = rep(0, p), Sigma = True.Sigma)

# compute sparse, positive covariance estimator:
fout <- bspcov::sbmspcov(X = X, Sigma = diag(diag(cov(X))))
summary(fout, cols = c(1, 3, 4), rows = c(1, 3, 4))
summary(fout, cols = 1, rows = 1:p)