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fda.vi

fda.vi is an R package that implements a variational expectation-maximization (VEM) algorithm for smoothing one or multiple functional observations via basis function selection. The algorithm estimates all model parameters simultaneously and automatically, while accounting for within-curve correlation. It provides a fast, scalable alternative to Bayesian sampling-based MCMC methods for smoothing functional curves with within-curve correlated errors via an Ornstein-Uhlenbeck covariance function.

The details of the variational EM algorithm can be found in da Cruz, de Souza and Sousa (2024).

Installation

# install.packages("pak")
pak::pak("desouzalab/fda.vi")

Quick Start

library(fda.vi)

data(toy_curves)

fit <- vem_fit(
  y      = toy_curves$y,
  Xt     = toy_curves$Xt,
  K      = 8,
  center = FALSE,
  scale  = FALSE
)

summary(fit)
#> ------------------------------------------------
#>  VEM Smooth Fit Summary
#> ------------------------------------------------
#> Basis Type            cubic_bspline 
#> Curves (m):           3 
#> Basis Functions (K):  8 
#> Active Bases (per curve): 6, 6, 6 
#> 
#> Model Parameters (Representative):
#>   Point estimate for decay parameter (w): 6.217 
#> 
#>   Posterior q(sigma^2) ~ IG(delta1, delta2):
#>    delta1 (shape):  97 
#>     delta2 (scale):  0.859869 
#> 
#>   Posterior q(tau^2) ~ IG(lambda1, lambda2):
#>     lambda1 (shape): 12.001 
#>    lambda2 (scale): 1517.36 
#> ------------------------------------------------

plot(fit, curve_idx = 1)

coef(fit)
#>      Curve_1    Curve_2    Curve_3
#> B1  1.5105434  1.6158541  1.5027916
#> B2  0.0000000  0.0000000  0.0000000
#> B3 -1.0173887 -1.1296271 -0.9889452
#> B4  0.4855913  0.7182764  0.8098905
#> B5  0.0000000  0.0000000  0.0000000
#> B6 -0.4088634 -0.7414497 -0.5324928
#> B7  1.0851575  1.1127756  1.2152763
#> B8 -1.0093923 -1.1186148 -0.9470451

Xt_new <- seq(0, 1, length.out = 200)
y_pred <- predict(fit, newdata = Xt_new)

head(y_pred[[1]])
#> [1] 1.5105434 1.3985729 1.2903498 1.1858269 1.0849569 0.9876925

Key Features

• Automatic Bayesian Basis Selection via sparsity-inducing priors on basis coefficients
• Uncertainty Quantification via 95% credible bands, constructed using the variational distributions of the basis coefficients and inclusion indicators
• Correlated (Within-Curve) Error Structure modelled explicitly using an Ornstein-Uhlenbeck covariance function, with the decay parameter estimated automatically in the M-step of the variational EM algorithm
• Automatic Selection of Number of Basis Functions K via generalized cross-validation (GCV) over a user-supplied grid of candidate basis sizes
• Multiple Basis Types supported: cubic B-splines and Fourier bases
• S3 User Methods: plot(), predict(), coef(), and summary() methods for fitted objects
• Fast The variational EM algorithm converges in tens of iterations

References

da Cruz, A. C., de Souza, C. P. E., & Sousa, P. H. T. O. (2024). Fast Bayesian basis selection for functional data representation with correlated errors. arXiv:2405.20758. https://arxiv.org/abs/2405.20758

Citation

To cite the method implemented in this package, please use:

da Cruz, A. C., de Souza, C. P. E., & Sousa, P. H. T. O. (2024). Fast Bayesian basis selection for functional data representation with correlated errors. arXiv:2405.20758. https://arxiv.org/abs/2405.20758

@misc{dacruz2024vem,
  title     = {Fast {Bayesian} basis selection for functional data
               representation with correlated errors},
  author    = {da Cruz, Ana Carolina and de Souza, Camila P. E. and
               Sousa, Pedro H. T. O.},
  year      = {2024},
  note      = {arXiv:2405.20758},
  url       = {https://arxiv.org/abs/2405.20758}
}

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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