Function-on-scalar regression models feature a function over some domain as the
response while the regressors are scalars. Collections of time series as well as
2D or 3D images can be considered as functional responses. We provide a hands-on
introduction for a flexible semiparametric approach for function-on-scalar
regression, using spatially referenced time series of ground velocity
measurements from large-scale simulated earthquake data as a running example. We
discuss important practical considerations and challenges in the modelling
process and outline best practices. The outline of our approach is complemented
by comprehensive R code, freely available in the online
appendix. This text is aimed at analysts with a working knowledge of generalized
regression models and penalized splines.
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