Predict population-level f0 smooth curves from a GAMM fit

Generates per-tone population-average smooth curves from a fitted fit_gamm() model. Useful for plotting predicted contours with confidence bands, comparing tones at a glance, or feeding into downstream Chao numeral summarisation via contour_to_chao().

Usage

predict_gamm(gamm_obj, n = 200)

Arguments

gamm_obj: An object of class "shinytone_gamm" from fit_gamm().
n: Number of time points across [0, 1]. Default 200.

Value

A data frame with columns time, f0_predicted, se, tone.

Details

Internally:

Build a per-tone time grid with n evenly-spaced points across [0, 1].
Set random-effect columns (speaker, item, and any random-smooth grouping factors) to the first level of their respective factors; these reference values are placeholders that don't affect the prediction once the corresponding terms are excluded.
Identify the random-effect terms that need to be excluded so the prediction reflects only the population-average fixed smooths.
Call stats::predict() on the mgcv model with exclude = <random terms> and se.fit = TRUE to also return standard errors.

Predictions are on the scale of the f0 column used to fit the model (typically semitones if you passed f0 = "f0_st" from normalise_f0()).

References

Sóskuthy, M. (2021). Evaluating generalised additive mixed modelling strategies for dynamic speech analysis. Journal of Phonetics, 84, 101017. doi:10.1016/j.wocn.2020.101017

Xu, C., & Zhang, C. (2024). A cross-linguistic review of citation tone production studies: Methodology and recommendations. The Journal of the Acoustical Society of America, 156(4), 2538–2565. doi:10.1121/10.0032356