The pseudo-residuals of momentuHMM models, as described in Zucchini and McDonad (2009).
pseudoRes(m, ncores = 1)
number of cores to use for parallel processing
If some turning angles in the data are equal to pi, the corresponding pseudo-residuals will not be included. Indeed, given that the turning angles are defined on (-pi,pi], an angle of pi results in a pseudo-residual of +Inf (check Section 6.2 of reference for more information on the computation of pseudo-residuals).
A continuity adjustment (adapted from Harte 2017) is made for discrete probability distributions. When the data are near the boundary (e.g. 0 for “pois”; 0 and 1 for “bern”), then the pseudo residuals can be a poor indicator of lack of fit.
For multiple imputation analyses, if
m is a
miHMM object or a list of
momentuHMM objects, then
the pseudo-residuals are individually calculated for each model fit. Note that pseudo-residuals for
miSum objects (as returned by
MIpool) are based on pooled parameter
estimates and the means of the data values across all imputations (and therefore may not be particularly meaningful).
m is a
miSum object, a list of pseudo-residuals for each data stream (e.g., 'stepRes', 'angleRes') is returned.
m is a list of
momentuHMM objects, then a list of length
length(m) is returned where each element is a list of pseudo-residuals for each data stream.
Harte, D. 2017. HiddenMarkov: Hidden Markov Models. R package version 1.8-8.
Zucchini, W. and MacDonald, I.L. 2009. Hidden Markov Models for Time Series: An Introduction Using R. Chapman & Hall (London).
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