qpgee: Quantile Penalized Generalized Estimating Equations (QPGEE)

In geeVerse: A Comprehensive Analysis of High Dimensional Longitudinal Data

Quantile Penalized Generalized Estimating Equations (QPGEE)

Description

Fits a quantile penalized generalized estimating equation (QPGEE) model for longitudinal data using penalized quantile regression with different working correlation structures.

Usage

qpgee(x, ...)

## S3 method for class 'formula'
qpgee(x, id, data = parent.frame(), ...)

## Default S3 method:
qpgee(
  x,
  y,
  nobs,
  tau = 0.5,
  corstr = "exchangeable",
  lambda = NULL,
  method = "HBIC",
  intercept = TRUE,
  betaint = NULL,
  nfold = 5,
  ncore = 1,
  control = qpgeeControl(),
  ...
)

Arguments

x	A matrix of predictors.
...	Other arguments passed to methods.
id	A vector identifying the clusters (subjects).
data	An optional data frame.
y	A numeric vector of response variables.
nobs	A numeric vector of observations per subject.
tau	The quantile to be estimated (default is 0.5).
corstr	A string specifying the working correlation structure. Options include "exchangeable" (Exchangeable), "AR1" (Autoregressive), "Tri" (Tri-diagonal), "independence" (Independent), and "unstructured".
lambda	A vector of penalty parameters. If NULL, auto-selection is performed.
method	Criterion for penalty selection ("HBIC" or "CV").
intercept	Logical; if TRUE, an intercept is added.
betaint	Initial values for the beta coefficients. If NULL, non-longitudinal quantile regression is used for initialization.
nfold	The number of folds used in cross-validation.
ncore	Number of cores for parallel processing.
control	A list of control parameters from 'qpgeeControl()', such as max_it, epsilon, shrinkCutoff, standardize and trace.

Value

An object of class 'qpgee'.

Examples

# Quick Example:
# 1. Generate some data
set.seed(123)
sim_data <- generate_data(
  nsub = 50, nobs = rep(5, 50), p = 10,
  beta0 = c(rep(1, 5), rep(0, 5)), rho = 0.3
)

# 2. Fit the model using the formula interface
fit <- qpgee(
  y ~ . - id,
  data = sim_data,
  id = sim_data$id,
  tau = 0.5,
  method = "HBIC"
)

# 3. View the summary of the results
summary(fit)

geeVerse documentation built on Aug. 21, 2025, 5:56 p.m.