sparsebn (version 0.1.2)

estimate.covariance: Covariance estimation

Description

Methods for inferring (i) Covariance matrices and (ii) Precision matrices for continuous, Gaussian data.

Usage

estimate.covariance(data, ...)
estimate.precision(data, ...)

Arguments

data

data as sparsebnData object.

...

(optional) additional parameters to estimate.dag

Value

Solution path as a plain list. Each component is a Matrix corresponding to an estimate of the covariance or precision (inverse covariance) matrix for a given value of lambda.

Details

For Gaussian data, the precision matrix corresponds to an undirected graphical model for the distribution. This undirected graph can be tied to the corresponding directed graphical model; see Sections 2.1 and 2.2 (equation (6)) of Aragam and Zhou (2015) for more details.

Examples

# NOT RUN {
data(cytometryContinuous)
dat <- sparsebnData(cytometryContinuous$data, type = "c", ivn = cytometryContinuous$ivn)
estimate.covariance(dat) # estimate covariance
estimate.precision(dat)  # estimate precision

# }