Compute a Covariance Matrix from a Fisher Information Matrix
Compute a Covariance Matrix from a Fisher Information Matrix
Stable function for computing a covariance matrix from a given Fisher information matrix by inversion.
invertinfo(mat, silent=TRUE, stopOnError=FALSE)
Arguments
mat: a Fisher Information Matrix.
silent: logical value. If FALSE, errors in the computation of the inverse while using the Cholesky decomposition algorithm are printed. If TRUE, errors can be seen only in the value error_message.
stopOnError: logical value. If TRUE only an error message is printed in case of error.
Details
A Cholesky decomposition is used to obtain the covariance matrix. This can be done because the Fisher information matrix is symmetric and positive definite.
This function is meant to be a more stable alternative to the function solve, which does not take into account, that the matrix is symmetric and positive definite.
Returns
A list containing the following components:
vcov: the covariance matrix.
error_message: possible error messages that occured when inverting the Fisher information matrix.
Author(s)
Tobias Liboschik and Philipp Probst
See Also
chol and chol2inv.
Examples
library(Matrix)invertinfo(Hilbert(5), stopOnError=TRUE)invertinfo(Hilbert(100))invertinfo(Hilbert(100), silent=FALSE)## Not run: invertinfo(Hilbert(100), stopOnError=TRUE)