Information about curves, surfaces, or anything else that varies over a continuum, such as growth over time is called functional data.
Time series data are an example of functional data. Functional data are often characterized by:
Functional data analysis (FDA) is a set of statistical tools that enables a more accurate summary and analysis of these types of data.
Principal Component Analysis (PCA) is a statistical procedure used to investigate and characterize dominant modes of variation in multivariate data, called principal components, or principal modes of variation. PCA is used across disciplines as a form of dimensionality reduction.
Analogously, Functional Principal Component Analysis (FPCA) is a method for investigating and characterizing the dominant modes of variation in functional data.
The visualization below (Figure 1) shows an example of FPCA inputs and output.
Flexible, data-driven approach for modeling growth trajectories and charactering patterns of variation in growth without imposing parametric functional form.[1, 4]
Semi-parametric model to describe growth length for age z-score (LAZ), where the only predictor is time (age).
Model is fit separately for each study.
Semi-parametric model to separately describe length, weight, and head circumference for ages 0-1 year, where the only predictor is time (age).
Describes fetal growth trajectories for all main ultrasound measures for gestational ages 14-43 weeks, where the only predictor is time (age).