MHPCA_decomp: Multilevel Hybrid Principal Component Analysis

In dsenturk/mhpca: Multilevel Hybrid Principal Components Analysis

Description

Function for performing MHPCA decomposition described in "Multilevel Hybrid Principal Components Analysis For Region-Referenced Functional EEG Data" by Campos et al. (202?), including estimation of fixed effects and marginal covariance functions, marginal eigencompoents, subject-specific scores, variance components, and measurement error variance.

Usage

MHPCA_decomp(
  data,
  fve_cutoff,
  nknots,
  maxiter = 1000,
  epsilon = 0.001,
  reduce = TRUE,
  quiet = FALSE
)

Arguments

data	dataframe in long format with six labeled columns (Repetition: (character vector), Subject: subject IDs (character vector), Group: subject group (character vector), func: functional argument (numeric vector), reg: regional argument (character vector), y: region-referenced functional data (numeric vector)) and row length equal to the length of the vectorized region-referenced Repetitions across all subjects and groups
fve_cutoff	fraction of variance cutoff for reducing the number of product components used in the mixed effects model (scalar in (0, 1))
nknots	number of knots to use for smoothing splines
maxiter	maximum number of iterations for MM algorithm (scalar)
epsilon	epsilon value for determining log-likelihood convergence (scalar)
reduce	should the number of product components be reduced and the mixed effects model re-estimated (logical)
quiet	display messages for timing (logical)

Value

A list with

• mu: the overall mean function (vector),

• eta: group-region-level-specific shifts (dataframe),

• covar: list with total, between and within covariance matrices,

• marg: list with between and within marginal covariances,

• model: list of models for each group, including scores and variances,

• data: data with all of the different pieces from the estimation,

• FVE: fraction of variance explained.

dsenturk/mhpca documentation built on Dec. 20, 2021, 2:11 a.m.