Package 'fastFGEE'

Simulated longitudinal functional example data

Description

A simulated dataset used in examples and testing for fastFGEE. The object d is a data frame containing a functional response stored in an AsIs matrix column together with a cluster identifier, two scalar covariates, and a longitudinal time variable.

Usage

data(d)

Format

A data frame with 5 variables:

Y

An AsIs matrix-valued column containing the functional response. In the included example, the matrix has 100 columns named Y_1 to Y_100.

ID

Cluster identifier.

X1

First scalar covariate.

X2

Second scalar covariate.

time

Longitudinal time variable.

Details

This dataset is intended for package examples, vignettes, and quick testing of fgee. It represents a simulated binary-response setting on a common functional grid.

Source

Simulated for the package examples.

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Fits functional generalized estimating equations (fGEE) for longitudinal functional outcomes using a one-step estimator, with optional fully iterated final estimation.

Description

Fits functional generalized estimating equations (fGEE) for longitudinal functional outcomes using a one-step estimator, with optional fully iterated final estimation.

Usage

fgee(
  formula,
  data,
  cluster,
  family,
  corr_fn = "ar1",
  corr_long = "ar1",
  time = NULL,
  long.dir = TRUE,
  var.type = "sandwich",
  pffr.mod = NULL,
  knots = NULL,
  bs = "bs",
  cv = "fastkfold",
  cv.grid = NULL,
  exact = FALSE,
  rho.smooth = FALSE,
  joint.CI = "wild",
  gee.fit = TRUE,
  linpred_method = c("accumulate", "matrix"),
  clip_mu = 0,
  m.pffr = c(2, 1),
  check_alignment = TRUE,
  max.iter = 1,
  tune.method = c("one-step", "fully-iterated"),
  boot.samps = 3000,
  ...
)

Arguments

formula	A model formula. The left-hand side should be a functional response stored as a matrix-like column (typically wrapped in I()).
data	A data frame containing the variables in formula.
cluster	Name of the cluster identifier column.
family	A family object or family name understood by refund::pffr().
corr_fn	Working correlation in the functional direction. One of "independent", "exchangeable", "ar1", or "fpca".
corr_long	Working correlation in the longitudinal direction. One of "independent", "exchangeable", or "ar1".
time	Optional name of the longitudinal ordering variable.
long.dir	Logical; retained for backwards compatibility.
var.type	Variance estimator. One of "sandwich", "fastboot", or "boot". For bootstraping, we recommend "fastboot" over "boot" because it is more thoroughly tested.
pffr.mod	Optional fitted refund::pffr() object to use as the initial estimator.
knots	Number of spline knots for the initial pffr() fit.
bs	Basis type passed to refund::pffr().
cv	Cross-validation mode used for smoothing parameter selection.
cv.grid	Optional grid (or staged grids) of smoothing parameters.
exact	Logical; for Gaussian identity-link models, use the exact penalized weighted least-squares update.
rho.smooth	Logical; smooth pointwise correlation estimates over the relevant index when applicable.
joint.CI	Logical or character controlling joint confidence intervals.
gee.fit	Logical; if FALSE, return uncertainty quantification around the initial fit without the GEE update.
linpred_method	Method used to form linear predictors.
clip_mu	Lower bound used for numerical stabilization of fitted means.
m.pffr	Penalty order specification passed to refund::pffr().
check_alignment	Logical; check alignment between the wide data and the pffr() long representation.
max.iter	Maximum number of GEE iterations. 1 gives the one-step fit.
tune.method	Smoothing-parameter tuning method.
boot.samps	Number of bootstrap replicates used when applicable.
...	Additional arguments reserved for future use.

Value

A "fgee1step" object. pffr_initial.fit contains the initial fit refund::pffr object. vb contains the variance/covariance matrix for the coefficient estimates (sandwich or bootstrap-based). qn contains the joint CI quantiles. di and \ wi are lists of length N, with the updated cluster-specific estimating equation and hessian terms (without the penalty).

Author(s)

Gabriel Loewinger [email protected]

References

Gabriel Loewinger, Alex W. Levis, Erjia Cui, and Francisco Pereira. (2025). Fast Penalized Generalized Estimating Equations for Large Longitudinal Functional Datasets. arXiv:2506.20437.

Examples

## Not run: 
library(fastFGEE)
data("data", package = "fastFGEE")
fit <- fgee(
 formula = Y ~ X1 + X2,
 data = d,
 cluster = "ID",
 family = binomial(link = "logit"),
 time = "time",
 corr_long = "exchangeable",
 corr_fn = "independent")

 fgee.plot(fit)
 
## End(Not run)

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Plot coefficient estimates from a fitted fastFGEE model

Description

Produces coefficient plots with pointwise and joint confidence intervals when available.

Usage

fgee.plot(
  fit,
  num_row = NULL,
  xlab = "Functional Domain",
  title_names = NULL,
  ylim = NULL,
  align_x = NULL,
  x_rescale = 1,
  y_val_lim = 1.1,
  y_scal_orig = 0.05,
  return = FALSE,
  terms.plot = TRUE,
  all.terms = TRUE,
  int.uncertainty = FALSE
)

Arguments

fit	A fitted object returned by fgee.
num_row	Number of rows used when arranging plots.
xlab	X-axis label.
title_names	Optional replacement titles for coefficient plots.
ylim	Optional y-axis limits.
align_x	Optional value used to re-center the x-axis.
x_rescale	Optional x-axis rescaling factor.
y_val_lim	Expansion factor for the upper y-axis limit.
y_scal_orig	Expansion factor for the lower y-axis limit.
return	Logical; if TRUE, return the plotting data instead of only drawing the plots.
terms.plot	Logical; retained for backwards compatibility.
all.terms	Logical; retained for backwards compatibility.
int.uncertainty	Logical; retained for backwards compatibility.

Value

Invisibly returns a list of plotting data frames when return = TRUE; otherwise draws plots.

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