Statistics and Pseudoreplication

Statistics and pseudoreplication

Applies to: Track2Data v0.1.0 and later.

Track2Data computes the numbers; it cannot choose your unit of replication. This page is about the decision that most often invalidates a behavioural-tracking analysis: treating frames, or animals sharing a tank, as independent observations.

The short version of the traps is in examples/README.md, and both starter scripts in examples/analysis/ implement the model described below. This page is the longer form: which unit applies to which metric family, and what to do about it.

The nesting Track2Data exports

frame  →  individual (individual_id)  →  session (session_id)  →  treatment / batch / day

Frames are never the unit of replication. They are autocorrelated by construction — an animal's position at frame t+1 is mostly its position at frame t — so a per-frame test on 20,000 frames reports a precision you do not have. Every summary metric already aggregates over frames; use the summary.

Animals within a session are not independent either. They see each other, share water, and share every disturbance. For group-living species that is not a nuisance term, it is the biology.

What the unit of replication is, by metric family

Family	What a row is	Unit of replication	Model
IL-* individual locomotion	one per animal per session	the animal, nested in session	metric ~ treatment + (1 | session_id)
Z-3 … Z-9 zone metrics that follow a slot	one per animal per zone	the animal, nested in session; zone is a within-animal factor	+ (1 | session_id), zone as a fixed factor or analysed per zone
Z-1, Z-2, Z-8 occupancy	per animal, or pooled on identity-free sessions	the animal, or the session when pooled	check for an individual_id; pooled rows are session-level
GL-* group metrics	one per session	the session	ordinary regression on session-level rows; n = number of sessions
D-* diagnostics	per animal or per session	not an outcome — a covariate or a filter	use for weighting and exclusion, not as a dependent variable

The row that catches people out is GL-*. A tank of eight fish yields one nearest-neighbour distance, one polarisation, one cohesion index. Eight animals do not buy eight observations of a group property, so a shoaling study needs replicate tanks, not replicate fish.

With time bins switched on, bins are repeated measures within a session, not new sessions: add bin_index as a fixed effect (or a smooth term) and keep the session random effect.

Before pooling sessions at all

Two differences make sessions incomparable rather than merely variable:

• Frame rate scales speed, acceleration and path length.
• Group size changes every group metric by construction.

sessions.csv carries fps, duration_s, n_frames, n_animals and is_calibrated so this is checkable in one line, and both starter scripts warn before modelling. The engine also flags sessions whose idtracker.ai segmentation parameters, resolution_reduction or id_image_size differ — body length and area are defined by those, and identity matching across sessions needs them equal. Read PROJECT_SUMMARY.md from the run before you pool.

if (n_distinct(sessions$fps) > 1) stop("speed and path length are not comparable")

Totals, rates and unequal duration

path_length_*, visit_count, freezing_bout_count, zone transitions and every other count or total scale with how long the session ran. A session 20 % longer produces a 20 % larger value for identical behaviour.

• Prefer a rate (mean_speed_*, *_fraction, *_pct) when one exists.
• For counts, model the total with an exposure offset rather than dividing:

glmmTMB::glmmTMB(visit_count ~ treatment + offset(log(duration_s)) + (1 | session_id),
                 family = poisson, data = individual)

Bounded ratios need bounded models

time_pct, every frac_* column, active_fraction, freezing_fraction and the polarisation means live in [0, 1]; jacobs_d lives in [−1, 1]. A Gaussian model on these predicts impossible values near the bounds and understates the variance of extreme cases. Use beta regression (glmmTMB(family = beta_family()), betareg), or a binomial model on the underlying frame counts when you have them, or logit-transform and say so.

A fraction computed from few frames is far noisier than one computed from many, and the two look identical in a table. Join D-11 and either weight by n_frames_used or exclude individuals below a coverage threshold you state in your methods:

quality = long[long.metric_id == "D-11"].pivot_table(
    index=["session_id", "individual_id"], columns="column", values="value"
).reset_index()
usable = quality.loc[quality.frac_frames_used >= 0.80, ["session_id", "individual_id"]]

Pick the threshold before you look at the outcome, not after.

Headings are circular

heading_rad and the IL-11 circular statistics are angles. Do not take arithmetic means, SDs or ordinary regressions of them: the mean of 350° and 10° is 0°, not 180°. Use circular statistics (circular in R, scipy.stats.circmean, pingouin.circ_*); IL-11 already reports resultant_length and rayleigh_p for exactly this reason.

Preprocessing choices are analysis choices

Smoothing window, max_gap_frames and whether identity-switch correction ran all change the numbers — on the shipped example, the freezing-bout count moves 6.7 % across a preprocessing grid while path length moves 1.2 %. They are recorded in manifest.json; report them in your methods, and if a result sits near your significance threshold, check whether it survives a different window:

track2data sensitivity project.t2d.json -o sensitivity/

That writes a per-column coefficient of variation across a grid of smoothing windows and max_gap_frames values. It deliberately prints no robust/not-robust verdict: where that line sits depends on the effect size you are claiming.

Multiplicity

The long table makes it trivial to test 50 metrics against one treatment, and equally trivial to find a significant one by chance. Decide which metrics are primary before you run the analysis, state the rest as exploratory, and correct within families of related outcomes. metric_id is in every row, so pre-registering a list of ids is enough.

Reporting checklist

State, in the methods: Track2Data app_version and project_hash from manifest.json; the preprocessing settings; the calibration mode and whether values are in cm or body lengths; the coverage threshold and how many individuals it excluded; the bout criterion actually applied (bout_criterion_effective, min_bout_frames_used); the random-effects structure; and the unit of replication with n at that level — not the number of animals, unless that is genuinely the level you analysed.