FPCA: Functional principal component analysis

In marcopalma3/neurofundata: Functional data analysis tools for neuroimaging data

Description

Return eigenimages, eigenvectors and scores.

Usage

FPCA(
  basis_mat,
  knot_space,
  pve_threshold,
  data_projected_name = paste0("data_projected_", knot_space, "mm.dat"),
  train_sub,
  test_sub = train_sub
)

Arguments

basis_mat	matrix of basis functions (each basis is vectorised in one column of the matrix)
knot_space	space between consecutive knots (in mm, equal for each dimension)
pve_threshold	proportion of variance explained
data_projected_name	text file with the smoothing projections for each statistical unit
train_sub	rownames of data_projected_name used to fit the FPCA
test_sub	rownames of data_projected_name for which to get the FPCA scores

Value

A list with the following elements

scores

Scores matrix (each row is for one statistical unit)

eFuns

Eigenimages (vectorised on each column)

eVals

Eigenvalues

Author(s)

Marco Palma, [e-mail]

marcopalma3/neurofundata documentation built on Dec. 12, 2019, 5:29 a.m.