Variance-covarience matrix (also simply called the 'covariance matrix') for the maximum-likelihood estimators of \(\beta_\mu\) and \(\beta_\sigma\). The matrix is calculated with the assumption of asymptotic normality of maximum likelihood estimators. This assumption is only valid in the limit of a large number of observations.
# S3 method for lmvar
vcov(object, mu = TRUE, sigma = TRUE, ...)Object of class 'lmvar'
Specifies whether or not the covariance matrix for \(\beta_\mu\) is included in the returned matrix
Specifies whether or not the covariance matrix for \(\beta_\sigma\) is included in the returned matrix
For compatibility with vcov generic
A 'matrix' object containing the (approximate) variance-covariance matrix of the maximum-likelihood estimators
of \(\beta_\mu\) and \(\beta_\sigma\) in object.
The variance-covariance matrix is calculated as \(I^{-1} / n\) where \(I\) is the Fisher information matrix and \(n\) the number of observations.
When mu = TRUE and sigma = TRUE, the full covariance matrix for the combined vector
\((\beta_\mu, \beta_\sigma)\) is returned.
When mu = TRUE and sigma = FALSE, only the covariance matrix for \(\beta_\mu\) is returned.
When mu = FALSE and sigma = TRUE, only the covariance matrix for \(\beta_\sigma\) is returned.
summary.lmvar for standard errors for \(\beta_\mu\) and \(\beta_\mu\).
nobs.lmvar_no_fit for the number of observations in an object of class 'lmvar'.
fisher for the Fisher information matrix of an object of class 'lmvar'.
See the vignette "Math" (to be viewed with vignette("Math", "lmvar")) for details.