santaR

Functional splines for Short AsyNchronous Time-series Analysis

Short time evolutions can be analysed by estimating each individual trajectory as a smooth spline through time.

Variables are representative of the underlying process and evolve smoothly through time.
Observations and measurements are noisy realisations of these underlying smooth functions of time.

Once each individual trajectory is estimated, the smooth curves become the new observational unit for further data analysis such as estimation of group curves and identification of time profiles significantly altered between groups.

The present approach is resilient to:

1 - Import Data

Data can be imported using a CSV file containing the raw data or a .RData file with the data and metaData dataframe. Previously fitted data can also be directly loaded for further analysis or plotting.

Optional - DF search

The degree of freedom (DF) parameter controls how closely the spline fits the input data (controlling overfitting).
This parameter is dependent on the study design (number of time-points, sampling rate, time-scale of the function of time under study) and therefore only needs to be selected once per dataset.

DF Search helps choose an adequate DF to apply across all variables for a given dataset:

2 - Analysis

Once the data imported, each variable can be fitted:

3 - Export

Input data, fitted data, as well as selected variables can be saved.

Copyright

The present software is licensed under GPLv3.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
See the GNU General Public License for more details. http://www.gnu.org/licenses/