plotPR: Plot pseudo-residuals

In bmcclintock/momentuHMM: Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

Plot pseudo-residuals

Description

Plots time series, qq-plots (against the standard normal distribution) using qqPlot, and sample ACF functions of the pseudo-residuals for each data stream

Usage

plotPR(m, lag.max = NULL, ncores = 1)

Arguments

m	A momentuHMM, momentuHierHMM, miHMM, HMMfits, or miSum object.
lag.max	maximum lag at which to calculate the acf. See acf.
ncores	number of cores to use for parallel processing

Details

• 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).

• If some data streams are zero-inflated and/or one-inflated, the corresponding pseudo- residuals are shown as segments, because pseudo-residuals for discrete data are defined as segments (see Zucchini and MacDonald, 2009, Section 6.2).

• For multiple imputation analyses, if m is a miHMM object or a list of momentuHMM objects, then the pseudo-residuals are individually calculated and plotted 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).

References

Zucchini, W. and MacDonald, I.L. 2009. Hidden Markov Models for Time Series: An Introduction Using R. Chapman & Hall (London).

Examples

# m is a momentuHMM object (as returned by fitHMM), automatically loaded with the package
m <- example$m

plotPR(m)

bmcclintock/momentuHMM documentation built on Oct. 26, 2022, 1 a.m.