FunQ

FunQ is an R package providing methodologies for functional data analysis from a quantile perspective. It implements robust Functional Quantile Principal Component Analysis (FQPCA) and Multilevel FQPCA (MFQPCA).

Unlike mean-based Functional PCA, FunQ allows you to model functional curves at specific quantile levels (e.g. median or extreme quantiles). This is particularly useful when functional curves exhibit skewness, subject-specific heteroscedasticity, or outliers.

Documentation Website

A complete, interactive documentation website is available at: 👉 https://alvaromc317.github.io/FunQ/

The website features:

• Getting Started with FQPCA: A comprehensive guide on single-level functional quantile decomposition, parameter selection, and plotting.
• Multilevel Analysis with MFQPCA: A detailed tutorial on handling repeated-measures or hierarchical functional data.
• Function Reference Index: Detailed descriptions and arguments for all exported functions.

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Installation

You can install the development version of FunQ from GitHub:

# install.packages("devtools")
devtools::install_github("alvaromc317/FunQ")

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Quick Start Example: FQPCA

Here is a quick demonstration of applying fqpca at the median ($q = 0.5$) with synthetic functional data.

First, load the libraries:

library(FunQ)
library(ggplot2)

1. Generating Data

We generate a synthetic dataset of 150 curves with 144 time points and 20% missing observations:

set.seed(5)

n.obs <- 150
n.time <- 144
Y.axis <- seq(0, 2*pi, length.out = n.time)

# Principal component loadings
pc1 <- sin(Y.axis)
pc2 <- cos(Y.axis)

# Subject-specific scores
c1.vals <- rnorm(n.obs, mean=0, sd=1)
c2.vals <- rnorm(n.obs, mean=0, sd=0.8)

# Construct curves and add noise + missing values
Y <- c1.vals %*% t(pc1) + c2.vals %*% t(pc2) + matrix(rnorm(n.obs * n.time, mean=0, sd=0.4), nrow = n.obs)
Y[sample(n.obs * n.time, as.integer(0.2 * n.obs * n.time))] <- NA

2. Fit the Model

Fit the Functional Quantile PCA model:

results <- fqpca(
  data = Y,
  npc = 2,
  quantile.value = 0.5,
  periodic = FALSE,
  splines.df = 10,
  seed = 5
)

3. Plot Loading Functions

Use the package’s generic plot method to visualize the estimated loading functions:

plot(results, pve = 0.95) +
  theme_minimal() +
  ggtitle("FQPCA Loading Functions at Median (q = 0.5)")

4. Cross-Validation

Use fqpca_cv_df to optimize the degrees of freedom parameter:

df_grid <- c(5, 10, 15)
cv_result <- fqpca_cv_df(
  data = Y,
  splines.df.grid = df_grid,
  n.folds = 3,
  criteria = "points",
  periodic = FALSE,
  seed = 5,
  verbose.cv = FALSE
)

# Mean error per grid value across folds
rowMeans(cv_result$error.matrix)
#> [1] 0.1662699 0.1640000 0.1639919

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Citation

If you use FunQ in your research, please cite the corresponding methodology papers:

• Álvaro Méndez-Civieta, Ying Wei, Keith M Diaz, Jeff Goldsmith. (2025). Functional quantile principal component analysis. Biostatistics, 26(1), kxae040. DOI: 10.1093/biostatistics/kxae040
• Álvaro Méndez-Civieta, Ying Wei, Jeff Goldsmith. (2026). Multilevel functional quantile principal component analysis. Biostatistics, kxag017. DOI: 10.1093/biostatistics/kxag017