Score residuals for detecting outlying subjects.
scoreresid.msm(x, plot = FALSE)Vector of the residuals, named by subject identifiers.
A fitted multi-state model, as returned by msm.
If TRUE, display a simple plot of the residuals in
subject order, labelled by subject identifiers
Andrew Titman a.titman@lancaster.ac.uk (theory), Chris Jackson chris.jackson@mrc-bsu.cam.ac.uk (code)
The score residual for a single subject is
$$U(\theta)^T I(\theta)^{-1} U(\theta)$$
where \(U(\theta)\) is the vector of first derivatives of the log-likelihood for that subject at maximum likelihood estimates \(\theta\), and \(I(\theta)\) is the observed Fisher information matrix, that is, the matrix of second derivatives of minus the log-likelihood for that subject at theta.
Subjects with a higher influence on the maximum likelihood estimates will have higher score residuals.
These are only available for models with analytic derivatives (which includes all non-hidden and most hidden Markov models).