scoreresid.msm(x, plot=FALSE)msm.TRUE, display a simple plot of the residuals in
subject order, labelled by subject identifiers$$U(\theta)^T I(\theta)^{-1} U(\theta)$$ 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).