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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
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[](https://github.com/jdtuck/fdasrvf_R/actions/workflows/check-standard.yaml)
[](https://github.com/jdtuck/fdasrvf_R/actions/workflows/test-coverage.yaml)
[](https://app.codecov.io/gh/jdtuck/fdasrvf_R?branch=master)
[](https://CRAN.R-project.org/package=fdasrvf)
<!-- badges: end -->
A R package for functional data analysis using the square root
velocity framework which performs pair-wise and group-wise
alignment as well as modeling using functional component
analysis
## Installation
v2.3.4 is on [CRAN](https://cran.r-project.org/package=fdasrvf)
and can be installed as
``` r
install.packages("fdasrvf")`
```
For a more up to date, but may not be stable version from git repository.
1. Download zip or tar.gz of package or clone repository
2. Install into R (> 4.3.0)
``` r
library(devtools)
install_github("jdtuck/fdasrvf_R")
```
## Example
The package contains `simu` dataset that is handy to experiment with the available functions for functions in $\mathbb{R}^1$.
We first visualize this dataset:
For that we first turn on class of functions present in the `f` for visualization:
```{r 1d_curve_plot}
library(fdasrvf)
f_plot(simu_data$time, simu_data$f)
```
We can see that each `curve` is a functionally closed 2D curve. And we distinguish different patterns
of miss-alignment, like X values shrinking, small displacement, and many others.
We will now proceed with curve alignment for the curves of this class 1:
```{r}
obj <- time_warping(simu_data$f, simu_data$time)
```
Let's plot the result
```{r 1d_aligned_plot}
plot(obj)
```
## References
Tucker, J. D. 2014, Functional Component Analysis and Regression using Elastic
Methods. Ph.D. Thesis, Florida State University.
Robinson, D. T. 2012, Function Data Analysis and Partial Shape Matching in the
Square Root Velocity Framework. Ph.D. Thesis, Florida State University.
Huang, W. 2014, Optimization Algorithms on Riemannian Manifolds with
Applications. Ph.D. Thesis, Florida State University.
Srivastava, A., Wu, W., Kurtek, S., Klassen, E. and Marron, J. S. (2011).
Registration of Functional Data Using Fisher-Rao Metric. arXiv:1103.3817v2.
Tucker, J. D., Wu, W. and Srivastava, A. (2013). Generative models for
functional data using phase and amplitude separation. Computational Statistics
and Data Analysis 61, 50-66.
J. D. Tucker, W. Wu, and A. Srivastava, "Phase-Amplitude Separation of
Proteomics Data Using Extended Fisher-Rao Metric," Electronic Journal of
Statistics, Vol 8, no. 2. pp 1724-1733, 2014.
J. D. Tucker, W. Wu, and A. Srivastava, "Analysis of signals under compositional
noise With applications to SONAR data," IEEE Journal of Oceanic Engineering, Vol
29, no. 2. pp 318-330, Apr 2014.
Srivastava, A., Klassen, E., Joshi, S., Jermyn, I., (2011). Shape analysis of
elastic curves in euclidean spaces. Pattern Analysis and Machine Intelligence,
IEEE Transactions on 33 (7), 1415-1428.
S. Kurtek, A. Srivastava, and W. Wu. Signal estimation under random
time-warpings and nonlinear signal alignment. In Proceedings of Neural
Information Processing Systems (NIPS), 2011.
Kurtek, S., Srivastava, A., Klassen, E., and Ding, Z. (2012), “Statistical
Modeling of Curves Using Shapes and Related Features,” Journal of the American
Statistical Association, 107, 1152–1165.
Wen Huang, Kyle A. Gallivan, Anuj Srivastava, Pierre-Antoine Absil. "Riemannian
Optimization for Elastic Shape Analysis", Short version, The 21st International
Symposium on Mathematical Theory of Networks and Systems (MTNS 2014).
Cheng, W., Dryden, I. L., and Huang, X. (2016). Bayesian registration of functions
and curves. Bayesian Analysis, 11(2), 447-475.
W. Xie, S. Kurtek, K. Bharath, and Y. Sun, A geometric approach to visualization
of variability in functional data, Journal of American Statistical Association 112
(2017), pp. 979-993.
Lu, Y., R. Herbei, and S. Kurtek, 2017: Bayesian registration of functions with a Gaussian process prior. Journal of
Computational and Graphical Statistics, 26, no. 4, 894–904.
Lee, S. and S. Jung, 2017: Combined analysis of amplitude and phase variations in functional data. arXiv:1603.01775, 1–21.
J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic Functional Principal Component Regression,” Statistical Analysis and Data Mining, vol. 12, no. 2, pp. 101-115, 2019.
J. D. Tucker, J. R. Lewis, C. King, and S. Kurtek, “A Geometric Approach for Computing Tolerance Bounds for Elastic Functional Data,” Journal of Applied Statistics, 10.1080/02664763.2019.1645818, 2019.
T. Harris, J. D. Tucker, B. Li, and L. Shand, "Elastic depths for detecting shape anomalies in functional data," Technometrics, 10.1080/00401706.2020.1811156, 2020.
Q. Xie, S. Kurtek, E. Klassen, G. E. Christensen and A. Srivastava. Metric-based pairwise and multiple image registration. IEEE European Conference on Computer Vision (ECCV), September, 2014
X. Zhang, S. Kurtek, O. Chkrebtii, and J. D. Tucker, “Elastic kkk-means clustering of functional data for posterior exploration, with an application to inference on acute respiratory infection dynamics”, arXiv:2011.12397 [stat.ME], 2020 arxiv
J. D. Tucker and D. Yarger, “Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources”, Envirometrics, 10.1002/env.2826, 2023.