In this vignette, we walk through periodogram for movement data and periodic movement model fitting and selection. It is assumed that you are already familiar with data preparation with ctmm, as well as the maximum likelihood procedure described in the `variogram`

vignette. Our example maned wolf data is already prepared into a `telemetry`

object.

```
library(ctmm)
data(wolf)
Gamba <- wolf$Gamba
plot(Gamba)
```

Before anything else, we want to plot the data in a way that makes periodic patterns apparent. This is the periodogram.

`LSP <- periodogram(Gamba,fast=2,res.time=2)`

The `fast=2`

option requests the use of the (much) faster FFT-based algorithm and furthermore samples a highly composite number of times. Set the argument to FALSE to revert to Scargle’s original algorithm, which involves fewer numerical approximations. The `res.time`

argument increases the resolution of the temporal grid when `fast>0`

. The algorithm defaults to adequate resolution for regularly scheduled data (permitting gaps), while variable sampling rates require `res.time>1`

to resolve the fine scale spectrum correctly.

`plot(LSP,max=TRUE,diagnostic=TRUE,cex=0.5)`

The `max=TRUE`

option keeps only local maxima and often yields periodograms that are easier to interpret, especially when the resolution of the periodogram has been increased from the default.

Periodicities in the data cause peaks at their respective periods on the horizontal axis. To visually assess significance, these peaks should be compared to the normal variation around the smooth trend of the periodogram. Important natural periods like the stellar day, synodic month and tropical year are labeled on the horizontal axes and given dark vertical lines in the plot. Harmonics of these natural periods have unlabeled tick marks on the horizontal axis and are are given lighter vertical lines in the plot. E.g., the two tick marks under a day represent the second and third harmonic of the day, or (24/2) 12 hours and (24/3) 8 hours, respectively Peaks at the harmonics of a period indicate that fine details of the periodicity are resolved by the data.

The `diagnostic=TRUE`

option draws the periodogram of the sampling schedule with red symbols. If the periodogram of the sampling schedule exhibits peaks, this indicates that the corresponding peaks in the movement data could be simply caused by irregularities in the sampling schedule and not by periodicity in the movement. Here the periodogram exhibits a clear peak for the period of one day, but this is also present in the (red) diagnostic.

Periodograms are a great data exploration tool, but they do not detect everything. In particular circulation processes, those that induce circulatory patterns via a stochastic rotational effect, are often not visible in periodograms. Periodograms are also difficult to compare from one individual to the next, if the aperiodic stochastic movements of these individuals are not the same.

Circulatory patterns can be incorporated into stochastic continuous time movement models in two ways: via the mean of the process, modelling that the animal reverts to a point that moves periodically through time, or via the stochastic component, by including a rotation effect. The first is called a periodic-mean process, and the second is called a circulation process.

For periodic-mean processes, you need to specify one or more period values that you want `ctmm`

to consider.

In our example, we are going to specify a period of a day, which is what the periodogram was saying. Other individuals in this population were also exhibiting a weekly periodicity, and in similar species, lunar cycles are well-known to affect ranging behaviors.

`PROTO <- ctmm(mean="periodic",period=c(24 %#% "hours",1 %#% "month"),circle=TRUE)`

The `mean="periodic"`

option tells `ctmm`

that you want a periodic-mean process. The default is `mean="stationary"`

. The period values are all specified in seconds (SI unit) by the `%#%`

function (see `help("%#%")`

for more information).

The `circle=TRUE`

option tells ctmm that you want to try and fit a model in which there is stochastic circulation on top of the periodicity in the process mean. For circulation processes, you do not need to specify candidate period values. The period of the circulation is estimated from the data.

As with other uses of ctmm, the next step is to generate an initial guess of the parameter values to feed to the likelihood optimization routine. You can do this using your intuition, by visually examining the variogram, or by letting `ctmm`

do it for you.

```
SVF <- variogram(Gamba,res=3)
GUESS <- ctmm.guess(Gamba,PROTO,variogram=SVF,interactive=FALSE)
```

`ctmm.guess`

is a generalization of `variogram.fit`

that can estimate other quantites from the data that are not apparent in the variogram, such as the circulation period and location correlations. A variogram argument is not necessary, but here we used the `res`

option to increase the FFT variogram’s temporal resolution and counteract sampling variability. The `interactive`

argument works just as with `variogram.fit`

.

Potentially, the most complex model based on our prototype could have both circulation and multiple harmonics of daily periodicity. In addition, we also do not know whether the velocities are autocorrelated in time (OUF model) and whether the animal moves amounts in different directions (anisotropy). All of this makes for a large number of effects. Forcing all these effects upon data that do not support them would inflate the risk of overfitting or convergence issues. We thereby conduct a model selection.

```
# CRAN policy limits to 2 processes (cores)
FITS <- ctmm.select(Gamba,GUESS,verbose=TRUE,cores=2)
```

`## Nyquist frequency estimated at harmonic 3 88.5917638888889 of the period.`

`# if you get errors on your platform, then try cores=1`

With `verbose=TRUE`

, we obtain a list fitted `ctmm`

objects, one for each relevant combination of effects, where the first element of that list is the preferred model.

`summary(FITS)`

```
## ΔAICc ΔRMSPE (m) DOF[area]
## OUF anisotropic harmonic 2 0 0.000000 60.815821 175.8871
## OUF anisotropic harmonic 1 0 20.689425 63.785896 179.2759
## OUF anisotropic harmonic 2 1 5.493885 89.639597 169.4066
## OUF anisotropic harmonic 1 1 26.103584 91.796917 172.8667
## OUF anisotropic harmonic 0 0 123.673098 157.298556 205.5584
## OUF anisotropic harmonic 0 1 128.448440 177.809090 200.7891
## OUF anisotropic harmonic 3 0 -17.231893 2496.228990 174.7865
## OUf anisotropic harmonic 2 0 19.477244 0.000000 237.2510
## OUf anisotropic harmonic 1 0 39.906174 5.147749 240.7653
## OUf anisotropic harmonic 0 0 124.354647 131.232070 233.3833
## OU anisotropic harmonic 2 0 34.440063 67.757512 146.6522
## OU anisotropic harmonic 1 0 51.739611 69.857170 150.1455
## OU anisotropic harmonic 0 0 193.520195 177.510804 163.4152
## OUF isotropic harmonic 2 0 88.869590 236.982418 163.4619
## OUF isotropic harmonic 1 0 113.063969 238.575572 166.0153
## OUF isotropic harmonic 0 0 230.160697 323.668044 196.4267
## OUF anisotropic circulation harmonic 0 0 124.426467 160.554313 204.4379
## OUF isotropic circulation harmonic 0 0 231.141563 327.779439 195.1977
```

From the first model, We see that the velocity autocorrelation (OUF), anisotropy, and `2`

harmonics of daily periodicity were all selected. Given how we specified the prototype, `harmonic 2 0`

means that this preferred model has no lunar periodicity, but has two harmonics of the one-day periodicity. This fittingly corresponds to what the periodogram was saying. If there was some moon-related pattern of space use and given how we specified our prototype, we would have had a non-zero value as the second `harmonic`

value.

The sorting of our candidate models is more complex than in previous stationary examples. For a given autocovariance model, the different non-stationary models are sorted by mean square predictive error (MSPE) and not the information criterion. As we will demonstrate, likelihood-based model selection can badly overfit with these types of models. For sorting between the autocovariance models (each with best non-stationary model), the information criterion is used. MSPE is not valid for general purpose selection.

```
# these are sorted by MSPE
SUB <- grepl("OUF anisotropic harmonic",names(FITS))
summary(FITS[SUB])
```

```
## ΔAICc ΔRMSPE (m) DOF[area]
## OUF anisotropic harmonic 2 0 0.000000 0.000000 175.8871
## OUF anisotropic harmonic 1 0 20.689425 2.970075 179.2759
## OUF anisotropic harmonic 2 1 5.493885 28.823776 169.4066
## OUF anisotropic harmonic 1 1 26.103584 30.981096 172.8667
## OUF anisotropic harmonic 0 0 123.673098 96.482735 205.5584
## OUF anisotropic harmonic 0 1 128.448440 116.993270 200.7891
## OUF anisotropic harmonic 3 0 -17.231893 2435.413169 174.7865
```

```
# these are sorted by IC
SUB <- grepl('0 0',names(FITS))
summary(FITS[SUB])
```

```
## ΔAICc ΔRMSPE (m) DOF[area]
## OUF anisotropic harmonic 0 0 0.0000000 26.06649 205.5584
## OUf anisotropic harmonic 0 0 0.6815490 0.00000 233.3833
## OUF anisotropic circulation harmonic 0 0 0.7533686 29.32224 204.4379
## OU anisotropic harmonic 0 0 69.8470969 46.27873 163.4152
## OUF isotropic harmonic 0 0 106.4875996 192.43597 196.4267
## OUF isotropic circulation harmonic 0 0 107.4684652 196.54737 195.1977
```

If only the information criterion was used, we would have selected `harmonic 3 0`

over `harmonic 2 0`

.

```
# sorting by IC only
summary(FITS,MSPE=NA)
```

```
## ΔAICc DOF[mean]
## OUF anisotropic harmonic 3 0 0.00000 106.67983
## OUF anisotropic harmonic 2 0 17.23189 105.76775
## OUF anisotropic harmonic 2 1 22.72578 103.79740
## OUf anisotropic harmonic 2 0 36.70914 149.25890
## OUF anisotropic harmonic 1 0 37.92132 107.66571
## OUF anisotropic harmonic 1 1 43.33548 105.71558
## OU anisotropic harmonic 2 0 51.67196 79.90435
## OUf anisotropic harmonic 1 0 57.13807 151.46287
## OU anisotropic harmonic 1 0 68.97150 81.79362
## OUF isotropic harmonic 2 0 106.10148 93.38189
## OUF isotropic harmonic 1 0 130.29586 94.69244
## OUF anisotropic harmonic 0 0 140.90499 131.12270
## OUf anisotropic harmonic 0 0 141.58654 149.16251
## OUF anisotropic circulation harmonic 0 0 141.65836 131.20181
## OUF anisotropic harmonic 0 1 145.68033 129.27965
## OU anisotropic harmonic 0 0 210.75209 88.86910
## OUF isotropic harmonic 0 0 247.39259 119.70643
## OUF isotropic circulation harmonic 0 0 248.37346 119.73743
```

Next we do some sanity checking on our results. The sampling interval for Gamba is fairly steady at

`"hour" %#% stats::median(diff(Gamba$t))`

`## [1] 4`

4 hours, which (ideally) corresponds to a Nyquist period of 8 (2 \(\times\) 4) hours, or 3 (24/8) times per day, or 3 harmonics of the day. The Nyquist period/frequency is an information limit on discretely sampled data. We expect to be able to extract a maximum of 3 harmonics from uniformly sampled data. Therefore, we should limit our consideration to harmonics of the day \(\leq\) 3, while for lower quality data, we might have to limit our consideration even further.

Consistent with these considerations, let us look at harmonics 3 and 2 of the day.

```
SUB <- rownames(summary(FITS,MSPE=NA))[1]
summary(FITS[[SUB]]) # harmonic 3 0 # selected by IC
```

```
## $name
## [1] "OUF anisotropic harmonic 3 0"
##
## $DOF
## mean area speed
## 106.6798346 174.7864589 0.8479798
##
## $CI
## low est high
## rotation/deviation % 74.360524 91.679710 100.000000
## rotation/speed % 96.735689 98.970180 100.000000
## area (square kilometers) 41.910582 48.890071 56.399203
## τ[position] (hours) 7.019750 9.335749 12.415855
## τ[velocity] (hours) 1.108651 1.599096 2.306504
## speed (kilometers/day) 12.330893 103.010397 205.063003
```

`summary(FITS[[1]]) # harmonic 2 0 # selected by IC/MSPE`

```
## $name
## [1] "OUF anisotropic harmonic 2 0"
##
## $DOF
## mean area speed
## 105.7677 175.8871 139.8130
##
## $CI
## low est high
## rotation/deviation % 23.4517770 28.855685 35.504796
## rotation/speed % 24.2563566 29.787735 36.580479
## area (square kilometers) 41.5574968 48.453880 55.871898
## τ[position] (hours) 7.2915577 9.600382 12.640280
## τ[velocity] (hours) 0.9719415 1.428103 2.098356
## speed (kilometers/day) 14.7661450 16.100485 17.433375
```

`rotation/deviation %`

corresponds to \(100 \eta_P\) from the Péron et al (2017). It is interpreted as the proportion of the variance in the animal`s location that is caused by the periodicity in the mean.`rotation/speed %`

corresponds to \(100 \eta_V\) from the Péron et al (2017). It is interpreted as the proportion of the variance in the animal`s velocity that is caused by the periodicity in the mean.`circulation period`

is the period of the stochastic circulations. On average, the animal re-pass through the same neighborhoods every estimated number of months (or days, or hours, depending on the automated unit specification).

Note that, aside from the rotational indices, these two models are largely consistent, and the 3 harmonic model has an extrordinarily uncertain speed estimate. We can get a better idea of what is happening by comparing the variograms.

```
xlim <- c(0,1/2) %#% "month"
plot(SVF,CTMM=FITS[[SUB]],xlim=xlim)
title("3 Harmonics")
plot(SVF,CTMM=FITS[[1]],xlim=xlim)
title("2 Harmonics")
```

While the confidence bands encompass the empirical variogram, the 3 harmonic model has clearly overfit in attempting to match the Nyquist period (3/day) with less than ideal data.

This is not coded yet!