Mixed models can be applied in a wide range of settings. Probably, they are most commonly used to handle grouping in the data. In addition, mixed models can be used for smoothing purposes as well. When dealing with non-normal data, the use of smoothing methods within the generalized linear mixed models (GLMM) framework is less familiar. We explore the use of GLMM for smoothing purposes in both spatial and longitudinal dimensions. The methodology is illustrated by analysis of spike potentials in the small intestine of different cats. Spatio-temporal models that use two-dimensional smoothing splines across the spatial dimension and random effects to account for the correlations during successive slow-waves are developed. A major advantage of the mixed-model approach is that it can handle smoothing together with grouping (or other types of correlations) in a unified model. In this way, areas with high spike incidence compared with other areas can be detected. Also, the temporal and spatial characteristics of spikes during successive slow-waves can be identified.
- Generalized linear mixed models
- Smoothing splines
- Spatio-temporal model
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty