psd_check {bnmonitor}  R Documentation 
Check for positive semidefiniteness after a perturbation
Description
psd_check
returns a boolean to determine if the covariance matrix after a perturbation is positive semidefinite.
Usage
psd_check(x, ...)
## S3 method for class 'GBN'
psd_check(x, entry, delta, ...)
## S3 method for class 'CI'
psd_check(x, type, entry, delta, ...)
Arguments
x 
object of class 
... 
additional arguments for compatibility. 
entry 
a vector of length 2 indicating the entry of the covariance matrix to vary. 
delta 
numeric vector, including the variation parameters that act additively. 
type 
character string. Type of modelpreserving covariation: either 
Details
The details depend on the class the method psd_check
is applied to.
Let \Sigma
be the covariance matrix of a Gaussian Bayesian network and let D
be a perturbation matrix acting additively. The perturbed covariance matrix \Sigma+D
is positive semidefinite if
\rho(D)\leq \lambda_{\min}(\Sigma)
where \lambda_{\min}
is the smallest eigenvalue end \rho
is the spectral radius.
Value
A dataframe including the variations performed and the check for positive semidefiniteness.
Methods (by class)

GBN
:psd_check
for objectsGBN

CI
:psd_check
for objectsCI
References
C. GĂ¶rgen & M. Leonelli (2020), Modelpreserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, 21: 132.
Examples
psd_check(synthetic_gbn,c(2,4),3)
psd_check(synthetic_gbn,c(2,3),seq(1,1,0.1))
psd_check(synthetic_ci,"partial",c(2,4),0.95)
psd_check(synthetic_ci,"all",c(2,3),seq(0.9,1.1,0.01))