# plotPR: Plot pseudo-residuals In momentuHMM: Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

## 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

 `1` ```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

 ```1 2 3 4``` ```# m is a momentuHMM object (as returned by fitHMM), automatically loaded with the package m <- example\$m plotPR(m) ```

momentuHMM documentation built on Sept. 5, 2021, 5:17 p.m.