functional_pca: Functional principal component analysis of disease progress...

In r4pde: Tools for Quantitative Plant Disease Epidemiology

Functional principal component analysis of disease progress curves

Description

Performs functional principal component analysis on fitted disease progress curves returned by functional_curves. The function decomposes variation among epidemic trajectories into orthogonal temporal components and returns curve-level scores, eigenfunctions, variance explained, and reconstructed curves.

Usage

functional_pca(object, ...)

## S3 method for class 'functional_curves'
functional_pca(
  object,
  n_components = NULL,
  var_explained = 0.95,
  center = TRUE,
  scale = FALSE,
  method = c("pca_on_grid"),
  ...
)

## S3 method for class 'functional_dsp'
functional_pca(
  object,
  n_components = NULL,
  var_explained = 0.95,
  center = TRUE,
  scale = FALSE,
  method = c("pca_on_grid"),
  ...
)

Arguments

object	An object returned by functional_curves.
...	Additional arguments for future extensions.
n_components	Optional integer number of functional principal components to retain.
var_explained	Cumulative variance threshold used when n_components = NULL.
center	Logical; whether to center curves before PCA. Default TRUE.
scale	Logical; whether to scale grid columns before PCA. Default FALSE.
method	Character; method for FPCA, currently only "pca_on_grid" is supported.

Details

The function uses the fitted curves from functional_curves() and does not refit the disease progress model. The first implementation uses PCA on a common prediction grid. FPC scores can be analyzed as functional epidemiological traits in downstream models.

Interpretation:

• FPC1 often captures the largest mode of variation, commonly overall epidemic intensity or speed.

• Later FPCs may capture timing of disease onset, curve crossing, late acceleration, or other shape-related deviations.

• Interpretation must be data-driven and should be based on eigenfunction plots and mean +/- perturbation plots.

Value

An object of class "r4pde_functional_pca" containing:

• scores: Tibble of curve-level FPC scores.

• eigenfunctions: Tibble of eigenfunction values across the time grid.

• variance: Tibble of eigenvalues, variance explained, and cumulative variance.

• mean_curve: Tibble of the mean curve.

• reconstructed: Tibble of reconstructed curves using retained components.

• input_curves: Tibble of original fitted curves.

• pca: The underlying prcomp object.

• settings: List of settings used.

• call: The matched call.

Examples

## Not run: 
curves <- functional_curves(...)
fpca <- functional_pca(curves, var_explained = 0.95)

print(fpca)
plot(fpca, type = "scree")
plot(fpca, type = "components")
plot(fpca, type = "scores", components = c(1, 2))

scores <- get_fpca_scores(fpca)

## End(Not run)

r4pde documentation built on Sept. 8, 2026, 1:10 a.m.