Functional Mixed Models

Functions for fitting functional mixed models with subject-level random effects. Functional Mixed Model

Usage

fmm(fdataobj, subject.ids, covariates = NULL, ncomp = 3)

Arguments

fdataobj

An object of class 'fdata'. All observations stacked (multiple per subject).

subject.ids

Integer vector of subject identifiers (same length as nrow(data)).

covariates

Optional matrix of covariates (n_total x p).

ncomp

Number of FPC components for random effects (default 3).

Value

An object of class 'fmm' with components:

mean.function

Overall mean function as fdata

beta.functions

Fixed effect coefficient functions as fdata

random.effects

Random effect functions per subject as fdata

fitted

Fitted values as fdata

residuals

Residuals as fdata

sigma2.eps

Residual variance estimate

sigma2.u

Random effect variance estimates

n.subjects

Number of unique subjects

Details

Fits a functional mixed model for repeated measures data: Y_ij(t) = mu(t) + sum_k x_ijk beta_k(t) + b_i(t) + eps_ij(t) where b_i(t) are subject-level random effects.

Examples

# \donttest{
# 10 subjects, 5 curves each = 50 total curves
fd <- fdata(matrix(rnorm(500), 50, 10), argvals = seq(0, 1, length.out = 10))
subject <- rep(1:10, each = 5)
fit <- fmm(fd, subject.ids = subject)
fit
#> Functional Mixed Model
#> ======================
#>   Number of observations: 50 
#>   Number of subjects: 10 
#>   FPC components: 3 
#>   Residual variance (sigma2_eps): 0.020122 
# }