Date: 2026-04-06

# Bayesian Function-on-Function Regression (FoFR)

#### 2026-04-06

# Introduction

This vignette provides a detailed guide to the `fofr_bayes()` function in the `refundBayes` package, which fits Bayesian Function-on-Function Regression (FoFR) models using Stan. FoFR extends both the scalar-on-function regression (SoFR) and function-on-scalar regression (FoSR) frameworks by modeling a functional response as a function of one or more functional predictors, along with optional scalar covariates.

In contrast to SoFR, where the outcome is scalar and the predictors are functional, and to FoSR, where the outcome is functional and the predictors are scalar, FoFR allows the effect of a functional predictor on a functional response to be captured through a bivariate coefficient function \(\beta(s, t)\). The model is specified using the same `mgcv`-style syntax as the other regression functions in the `refundBayes` package.

The methodology extends the framework described in Jiang, Crainiceanu, and Cui (2025), Tutorial on Bayesian Functional Regression Using Stan, published in Statistics in Medicine.

# Install the `refundBayes` Package

The `refundBayes` package can be installed from CRAN:

    install.packages("refundBayes")

For the latest version of the `refundBayes` package, users can install from GitHub:

    library(remotes)
    remotes::install_github("https://github.com/ZirenJiang/refundBayes")

# Statistical Model

## The FoFR Model

Function-on-Function Regression (FoFR) models the relationship between a functional response and one or more functional predictors, optionally adjusted for scalar covariates. Both the response and the predictors are curves observed over (potentially different) continua.

For subject \(i = 1, \ldots, n\), let \(Y_i(t)\) be the functional response observed at time points \(t = t_1, \ldots, t_M\) over a response domain \(\mathcal{T}\), and let \(\{W_i(s_l), s_l \in \mathcal{S}\}\) for \(l = 1, \ldots, L\) be a functional predictor observed at \(L\) points over a predictor domain \(\mathcal{S}\). Let \(\mathbf{X}_i = (X_{i1}, \ldots, X_{iP})^t\) be a \(P \times 1\) vector of scalar predictors (the first covariate may be an intercept, \(X_{i1} = 1\)). The FoFR model assumes:

\[Y_i(t) = \sum_{p=1}^P X_{ip}\,\alpha_p(t) + \int_{\mathcal{S}} W_i(s)\,\beta(s, t)\,ds + e_i(t)\]

where:

- \(\alpha_p(t)\) are functional coefficients for the scalar predictors, each describing how the \(p\)-th scalar predictor affects the response at each point \(t \in \mathcal{T}\),
- \(\beta(s, t)\) is the bivariate coefficient function that characterizes how the functional predictor at predictor-domain location \(s\) affects the response at response-domain location \(t\),
- \(e_i(t)\) is the residual process, which is correlated across \(t\).

The integral \(\int_{\mathcal{S}} W_i(s)\,\beta(s, t)\,ds\) is approximated using a Riemann sum over the observed predictor-domain grid points. The domains \(\mathcal{S}\) and \(\mathcal{T}\) are not restricted to \([0,1]\); they are determined by the actual observation grids in the data.

When multiple functional predictors are present, the model extends naturally:

\[Y_i(t) = \sum_{p=1}^P X_{ip}\,\alpha_p(t) + \sum_{j=1}^J \int_{\mathcal{S}_j} W_{ij}(s)\,\beta_j(s, t)\,ds + e_i(t)\]

where \(\beta_j(s, t)\) is the bivariate coefficient function for the \(j\)-th functional predictor.

## Modeling the Residual Structure

To account for the within-subject correlation in the residuals, the model decomposes \(e_i(t)\) using functional principal components, following the same approach as in FoSR (Goldsmith, Zipunnikov, and Schrack, 2015):

\[e_i(t) = \sum_{r=1}^R \xi_{ir}\,\phi_r(t) + \epsilon_i(t)\]

where \(\phi_1(t), \ldots, \phi_R(t)\) are the eigenfunctions estimated via FPCA (using `refund::fpca.face`), \(\xi_{ir}\) are the subject-specific FPCA scores with \(\xi_{ir} \sim N(0, \lambda_r)\), and \(\epsilon_i(t) \sim N(0, \sigma_\epsilon^2)\) is independent measurement error. The eigenvalues \(\lambda_r\) and the error variance \(\sigma_\epsilon^2\) are estimated from the data.

## Scalar Predictor Coefficients via Penalized Splines

Each scalar predictor coefficient function \(\alpha_p(t)\) is represented using \(K\) spline basis functions \(\psi_1(t), \ldots, \psi_K(t)\) in the response domain:

\[\alpha_p(t) = \sum_{k=1}^K a_{pk}\,\psi_k(t)\]

Smoothness is induced through a quadratic penalty:

\[p(\mathbf{a}_p) \propto \exp\left(-\frac{\mathbf{a}_p^t \mathbf{S} \mathbf{a}_p}{2\sigma_p^2}\right)\]

where \(\mathbf{S}\) is the penalty matrix derived from the spline basis and \(\sigma_p^2\) is the smoothing parameter for the \(p\)-th scalar predictor, estimated from the data.

## Bivariate Coefficient via Tensor Product Basis

The key feature of FoFR is the bivariate coefficient function \(\beta(s, t)\), which lives on the product domain \(\mathcal{S} \times \mathcal{T}\). This function is represented using a tensor product of two sets of basis functions:

- Predictor-domain basis: \(B_1(s), \ldots, B_Q(s)\), constructed from the `s()` term in the formula using the `mgcv` spline basis (e.g., cubic regression splines). These are further decomposed into random and fixed effect components via the spectral reparametrisation of `mgcv::smooth2random()`, yielding \(\tilde{B}_1^r(s), \ldots, \tilde{B}_{Q_r}^r(s)\) (random effects) and \(\tilde{B}_1^f(s), \ldots, \tilde{B}_{Q_f}^f(s)\) (fixed effects).

- Response-domain basis: \(\psi_1(t), \ldots, \psi_K(t)\), the same spline basis used for the scalar predictor coefficients.

The bivariate coefficient is then:

\[\beta(s, t) = \sum_{k=1}^K \left[\sum_{q=1}^{Q_r} \theta_{qk}^r\,\tilde{B}_q^r(s) + \sum_{q=1}^{Q_f} \theta_{qk}^f\,\tilde{B}_q^f(s)\right] \psi_k(t)\]

where \(\boldsymbol{\Theta}^r\) is the \(Q_r \times K\) matrix of random effect coefficients and \(\boldsymbol{\Theta}^f\) is the \(Q_f \times K\) matrix of fixed effect coefficients.

With this representation, the integral contribution of the functional predictor becomes:

\[\int_{\mathcal{S}} W_i(s)\,\beta(s, t)\,ds \approx \left[\tilde{\mathbf{X}}_{i}^r \boldsymbol{\Theta}^r + \tilde{\mathbf{X}}_{i}^f \boldsymbol{\Theta}^f\right] \boldsymbol{\psi}(t)\]

where \(\tilde{\mathbf{X}}_{i}^r\) and \(\tilde{\mathbf{X}}_{i}^f\) are the \(1 \times Q_r\) and \(1 \times Q_f\) row vectors of the transformed predictor-domain design matrices for subject \(i\) (the same matrices used in SoFR).

In matrix notation for all subjects, the functional predictor contribution to the mean is:

\[\left(\tilde{\mathbf{X}}^r \boldsymbol{\Theta}^r + \tilde{\mathbf{X}}^f \boldsymbol{\Theta}^f\right) \boldsymbol{\Psi}\]

which is an \(n \times M\) matrix, where \(\boldsymbol{\Psi}\) is the \(K \times M\) matrix of response-domain spline basis evaluations.

## Dual-Direction Smoothness Penalties

Because the bivariate coefficient \(\beta(s, t)\) lives on a two-dimensional domain, smoothness must be enforced in both directions:

### Predictor-domain smoothness (\(s\)-direction)

Following the same approach as in SoFR, the random effect coefficients use a variance-component reparametrisation:

\[\boldsymbol{\Theta}^r = \sigma_s \cdot \mathbf{Z}^r, \quad \text{vec}(\mathbf{Z}^r) \sim N(\mathbf{0}, \mathbf{I})\]

where \(\sigma_s^2\) is the predictor-domain smoothing parameter. This is the standard non-centered parameterisation that separates the scale (\(\sigma_s\)) from the direction (\(\mathbf{Z}^r\)), improving sampling efficiency in Stan.

### Response-domain smoothness (\(t\)-direction)

The response-domain penalty matrix \(\mathbf{S}\) is applied row-wise to the coefficient matrices. For each row \(q\) of \(\boldsymbol{\Theta}^r\) and \(\boldsymbol{\Theta}^f\):

\[p(\boldsymbol{\Theta}_q^r) \propto \exp\left(-\frac{\boldsymbol{\Theta}_q^r \mathbf{S}\,(\boldsymbol{\Theta}_q^r)^t}{2\sigma_t^2}\right), \qquad p(\boldsymbol{\Theta}_q^f) \propto \exp\left(-\frac{\boldsymbol{\Theta}_q^f \mathbf{S}\,(\boldsymbol{\Theta}_q^f)^t}{2\sigma_t^2}\right)\]

where \(\sigma_t^2\) is the response-domain smoothing parameter. This ensures that \(\beta(s, t)\) is smooth in the \(t\)-direction for every fixed \(s\).

The two smoothing parameters \(\sigma_s^2\) and \(\sigma_t^2\) are estimated from the data with weakly informative inverse-Gamma priors.

## Full Bayesian Model

The complete Bayesian FoFR model combines the mean structure, residual FPCA decomposition, and all priors:

\[\begin{cases} Y_i(t) = \boldsymbol{\mu}_i(t) + e_i(t), \quad i = 1, \ldots, n \\[6pt] \boldsymbol{\mu}_i(t) = \displaystyle\sum_{p=1}^P X_{ip}\,\alpha_p(t) + \int_{\mathcal{S}} W_i(s)\,\beta(s,t)\,ds \\[6pt] e_i(t) = \displaystyle\sum_{r=1}^R \xi_{ir}\,\phi_r(t) + \epsilon_i(t) \end{cases}\]

In matrix form for all subjects:

\[\mathbf{Y} = \mathbf{X}\,\mathbf{A}^t \boldsymbol{\Psi} + \left(\tilde{\mathbf{X}}^r \boldsymbol{\Theta}^r + \tilde{\mathbf{X}}^f \boldsymbol{\Theta}^f\right) \boldsymbol{\Psi} + \boldsymbol{\Xi}\,\boldsymbol{\Phi} + \boldsymbol{E}\]

where \(\mathbf{Y}\) is the \(n \times M\) matrix of functional responses, \(\mathbf{X}\) is the \(n \times P\) scalar design matrix, \(\mathbf{A}\) is the \(K \times P\) matrix of scalar predictor spline coefficients, \(\boldsymbol{\Xi}\) is the \(n \times R\) matrix of FPCA scores, and \(\boldsymbol{\Phi}\) is the \(R \times M\) matrix of eigenfunctions.

### Prior Specification

The full prior specification is:

Parameter | Prior | Description
--- | --- | ---
\(\mathbf{a}_p\) (scalar predictor spline coefs) | \(p(\mathbf{a}_p) \propto \exp\left(-\frac{\mathbf{a}_p^t \mathbf{S}\,\mathbf{a}_p}{2\sigma_p^2}\right)\) | Penalized spline prior for smoothness of \(\alpha_p(t)\)
\(\sigma_p^2\) (scalar smoothing parameter) | \(\sigma_p^2 \sim \text{Inv-Gamma}(0.001, 0.001)\) | Weakly informative prior on smoothing
\(\text{vec}(\mathbf{Z}^r)\) (standardized random effects) | \(\text{vec}(\mathbf{Z}^r) \sim N(\mathbf{0}, \mathbf{I})\) | Non-centered parameterisation for \(s\)-direction
\(\sigma_s^2\) (predictor-domain smoothing) | \(\sigma_s^2 \sim \text{Inv-Gamma}(0.0005, 0.0005)\) | Prior on predictor-domain smoothing
\(\boldsymbol{\Theta}_q^r, \boldsymbol{\Theta}_q^f\) (row-wise) | \(p(\boldsymbol{\Theta}_q) \propto \exp\left(-\frac{\boldsymbol{\Theta}_q \mathbf{S}\,\boldsymbol{\Theta}_q^t}{2\sigma_t^2}\right)\) | Penalized spline prior for response-domain smoothness
\(\sigma_t^2\) (response-domain smoothing) | \(\sigma_t^2 \sim \text{Inv-Gamma}(0.001, 0.001)\) | Prior on response-domain smoothing
\(\xi_{ir}\) (FPCA scores) | \(\xi_{ir} \sim N(0, \lambda_r)\) | FPCA score distribution
\(\lambda_r\) (FPCA eigenvalues) | \(\lambda_r^2 \sim \text{Inv-Gamma}(0.001, 0.001)\) | Weakly informative prior on eigenvalues
\(\sigma_\epsilon^2\) (residual variance) | \(\sigma_\epsilon^2 \sim \text{Inv-Gamma}(0.001, 0.001)\) | Weakly informative prior on noise variance

### Likelihood

The likelihood for the functional response is Gaussian:

\[\log p(\mathbf{Y} \mid \boldsymbol{\mu}, \sigma_\epsilon^2) = -\frac{nM}{2}\log\sigma_\epsilon - \frac{1}{2\sigma_\epsilon^2}\sum_{i=1}^n \sum_{m=1}^M \{Y_i(t_m) - \mu_i(t_m)\}^2\]

where the mean function \(\mu_i(t_m)\) includes contributions from scalar predictors, functional predictors, and FPCA scores.

## Optional: Joint FPCA Modeling

The default `fofr_bayes()` model treats the observed functional predictor \(W_i(s)\) as if it were observed without error. When the predictor curves are noisy, this attenuates the bivariate coefficient \(\beta(s,t)\) toward zero (errors-in-variables bias) and shrinks the credible-band width. The `joint_FPCA` argument activates an alternative model in which \(W_i(s)\) is replaced by an FPCA representation and the subject-specific FPC scores are sampled jointly with the bivariate coefficient, propagating the FPCA uncertainty into the posterior of \(\beta(s,t)\).

The same `joint_FPCA` argument is available in `sofr_bayes()` and `fcox_bayes()`. The full model specification (FPCA likelihood for the predictor, FPC-score prior centered on the initial `refund::fpca.sc()` scores, the resulting Stan program with bivariate coefficients in the FPC basis, and a worked FoFR example) is presented in the dedicated Joint FPCA vignette.

## Relationship to SoFR and FoSR

The FoFR model nests both the SoFR and FoSR models as special cases:

Model | Response | Predictors | Coefficient | Implemented in
--- | --- | --- | --- | ---
SoFR | Scalar \(Y_i\) | Functional \(W_i(s)\) | Univariate \(\beta(s)\) | `sofr_bayes()`
FoSR | Functional \(Y_i(t)\) | Scalar \(X_{ip}\) | Univariate \(\alpha_p(t)\) | `fosr_bayes()`
FoFR | Functional \(Y_i(t)\) | Functional \(W_i(s)\) + Scalar \(X_{ip}\) | Bivariate \(\beta(s,t)\) + Univariate \(\alpha_p(t)\) | `fofr_bayes()`

The `fofr_bayes()` function inherits:

- From FoSR: the response-domain spline basis \(\boldsymbol{\Psi}\), the FPCA residual structure, and the scalar predictor handling.
- From SoFR: the predictor-domain spectral reparametrisation (random/fixed effect decomposition via `mgcv::smooth2random()`), the basis extraction, and the functional coefficient reconstruction.

# The `fofr_bayes()` Function

## Usage

    fofr_bayes(
      formula,
      data,
      joint_FPCA = NULL,
      runStan = TRUE,
      niter = 3000,
      nwarmup = 1000,
      nchain = 3,
      ncores = 1,
      spline_type = "bs",
      spline_df = 10
    )

## Arguments

Argument | Description
--- | ---
`formula` | Functional regression formula, using the same syntax as `mgcv::gam`. The left-hand side is the functional response (an \(n \times M\) matrix in `data`). The right-hand side includes scalar predictors as standard terms and functional predictors via `s(..., by = ...)` terms. At least one functional predictor must be present; otherwise use `fosr_bayes()`.
`data` | A data frame containing all variables used in the model. The functional response and functional predictor should be stored as \(n \times M\) and \(n \times L\) matrices, respectively.
`joint_FPCA` | A logical (`TRUE`/`FALSE`) vector of the same length as the number of functional predictors, indicating whether to jointly model FPCA for each functional predictor. When `TRUE`, the observed functional predictor is replaced by an FPCA representation and its FPC scores are sampled jointly with the bivariate coefficient (errors-in-variables-aware fit). See the Joint FPCA vignette for the model specification and a worked FoFR example. Default is `NULL`, which sets all entries to `FALSE` (no joint FPCA).
`runStan` | Logical. Whether to run the Stan program. If `FALSE`, the function only generates the Stan code and data without sampling. This is useful for inspecting or modifying the generated Stan code. Default is `TRUE`.
`niter` | Total number of Bayesian posterior sampling iterations (including warmup). Default is `3000`.
`nwarmup` | Number of warmup (burn-in) iterations. These samples are discarded and not used for inference. Default is `1000`.
`nchain` | Number of Markov chains for posterior sampling. Multiple chains help assess convergence. Default is `3`.
`ncores` | Number of CPU cores to use when executing the chains in parallel. Default is `1`.
`spline_type` | Type of spline basis used for the response-domain component. Default is `"bs"` (B-splines). Other types supported by `mgcv` may also be used.
`spline_df` | Number of degrees of freedom (basis functions) for the response-domain spline basis. Default is `10`.

## Return Value

The function returns a list of class `"refundBayes"` containing the following elements:

Element | Description
--- | ---
`stanfit` | The Stan fit object (class `stanfit`). Can be used for convergence diagnostics, traceplots, and additional summaries via the `rstan` package.
`spline_basis` | Basis functions used to reconstruct the functional coefficients from the posterior samples.
`stancode` | A character string containing the generated Stan model code.
`standata` | A list containing the data passed to the Stan model.
`scalar_func_coef` | A 3-d array (\(Q \times P \times M\)) of posterior samples for scalar predictor coefficient functions \(\alpha_p(t)\), where \(Q\) is the number of posterior samples, \(P\) is the number of scalar predictors, and \(M\) is the number of response-domain time points. `NULL` if no scalar predictors.
`bivar_func_coef` | A list of 3-d arrays. Each element corresponds to one functional predictor and is an array of dimension \(Q \times L \times M\), representing posterior samples of the bivariate coefficient function \(\beta(s, t)\) evaluated on the predictor-domain grid (\(L\) points) and response-domain grid (\(M\) points).
`func_coef` | Same as `scalar_func_coef`; included for compatibility with the `plot.refundBayes()` method.
`family` | The model family: `"fofr"`.

## Formula Syntax

The formula combines the FoSR syntax (functional response on the left-hand side) with the SoFR syntax (functional predictors via `s()` terms on the right-hand side):

    Y_mat ~ X1 + X2 + s(sindex, by = X_func, bs = "cr", k = 10)

where:

- `Y_mat`: the name of the functional response variable in `data`. This should be an \(n \times M\) matrix, where each row contains the functional observations for one subject across \(M\) response-domain time points.
- `X1`, `X2`: scalar predictor(s), included using standard formula syntax.
- `s(sindex, by = X_func, bs = "cr", k = 10)`: the functional predictor term:
  - `sindex`: an \(n \times L\) matrix of predictor-domain grid points. Each row contains the same \(L\) observation points (replicated across subjects).
  - `X_func`: an \(n \times L\) matrix of functional predictor values. The \(i\)-th row contains the \(L\) observed values for subject \(i\).
  - `bs`: the type of spline basis for the predictor domain (e.g., `"cr"` for cubic regression splines).
  - `k`: the number of basis functions in the predictor domain.

The response-domain spline basis is controlled separately via the `spline_type` and `spline_df` arguments to `fofr_bayes()`. This design separates the two basis specifications: the predictor-domain basis is specified in the formula (as in SoFR), while the response-domain basis is specified via function arguments (as in FoSR).

Multiple functional predictors can be included by adding additional `s()` terms.

# Example: Bayesian FoFR with Simulated Data

We demonstrate the `fofr_bayes()` function using a simulation study with a known bivariate coefficient function \(\beta(s, t)\) and a scalar predictor coefficient function \(\alpha(t)\).

## Simulate Data

    library(refundBayes)

    set.seed(42)

    # --- Dimensions ---
    n  <- 200   # number of subjects
    L  <- 30    # number of predictor-domain grid points
    M  <- 30    # number of response-domain grid points

    sindex <- seq(0, 1, length.out = L)   # predictor domain grid
    tindex <- seq(0, 1, length.out = M)   # response domain grid

    # --- Functional predictor X(s): smooth random curves ---
    X_func <- matrix(0, nrow = n, ncol = L)
    for (i in 1:n) {
      X_func[i, ] <- rnorm(1) * sin(2 * pi * sindex) +
                     rnorm(1) * cos(2 * pi * sindex) +
                     rnorm(1) * sin(4 * pi * sindex) +
                     rnorm(1, sd = 0.3)
    }

    # --- Scalar predictor ---
    age <- rnorm(n)

    # --- True coefficient functions ---
    # Bivariate coefficient: beta(s, t) = sin(2*pi*s) * cos(2*pi*t)
    beta_true <- outer(sin(2 * pi * sindex), cos(2 * pi * tindex))

    # Scalar coefficient function: alpha(t) = 0.5 * sin(pi*t)
    alpha_true <- 0.5 * sin(pi * tindex)

    # --- Generate functional response ---
    # Y_i(t) = age_i * alpha(t) + integral X_i(s) beta(s,t) ds + epsilon_i(t)
    signal_scalar <- outer(age, alpha_true)                    # n x M
    signal_func   <- (X_func %*% beta_true) / L               # n x M  (Riemann sum)
    epsilon        <- matrix(rnorm(n * M, sd = 0.3), nrow = n) # n x M

    Y_mat <- signal_scalar + signal_func + epsilon

    # --- Organize data ---
    dat <- data.frame(age = age)
    dat$Y_mat  <- Y_mat
    dat$X_func <- X_func
    dat$sindex <- matrix(rep(sindex, n), nrow = n, byrow = TRUE)

The simulated dataset `dat` contains:

- `Y_mat`: an \(n \times M\) matrix of functional response values,
- `age`: a scalar predictor,
- `X_func`: an \(n \times L\) matrix of functional predictor values (smooth random curves),
- `sindex`: an \(n \times L\) matrix of predictor-domain grid points (identical rows).

The true data-generating model is: \[Y_i(t) = \text{age}_i \cdot 0.5\sin(\pi t) + \frac{1}{L}\sum_{l=1}^L X_i(s_l)\,\sin(2\pi s_l)\cos(2\pi t) + \epsilon_i(t), \quad \epsilon_i(t) \sim N(0, 0.3^2)\]

## Fit the Bayesian FoFR Model

    fit_fofr <- fofr_bayes(
      formula     = Y_mat ~ age + s(sindex, by = X_func, bs = "cr", k = 10),
      data        = dat,
      spline_type = "bs",
      spline_df   = 10,
      niter       = 2000,
      nwarmup     = 1000,
      nchain      = 3,
      ncores      = 3
    )

In this call:

- The formula specifies the functional response `Y_mat` (an \(n \times M\) matrix) with one scalar predictor `age` and one functional predictor `X_func`.
- The predictor-domain spline basis uses cubic regression splines (`bs = "cr"`) with `k = 10` basis functions.
- The response-domain spline basis uses B-splines (`spline_type = "bs"`) with `spline_df = 10` degrees of freedom.
- The eigenfunctions for the residual structure are estimated automatically via FPCA using `refund::fpca.face`.
- The sampler runs 3 chains in parallel, each with 2000 total iterations (1000 warmup + 1000 posterior samples).

### A Note on Computation

FoFR models are the most computationally demanding among the models in `refundBayes` because the Stan program estimates bivariate coefficient matrices (with \(Q_r \times K + Q_f \times K\) parameters per functional predictor) in addition to the scalar predictor coefficients and FPCA scores. For exploratory analyses, consider using fewer basis functions (e.g., `k = 5`, `spline_df = 5`) and a single chain. For final inference, use the full setup with multiple chains and convergence diagnostics.

## Visualisation

### Bivariate Coefficient \(\hat{\beta}(s, t)\)

The estimated bivariate coefficient \(\hat{\beta}(s,t)\) is stored as a 3-d array in `bivar_func_coef`. The posterior mean surface and comparison with the truth can be visualised using heatmaps:

    # Posterior mean of the bivariate coefficient
    beta_est  <- apply(fit_fofr$bivar_func_coef[[1]], c(2, 3), mean)

    # Pointwise 95% credible interval bounds
    beta_lower <- apply(fit_fofr$bivar_func_coef[[1]], c(2, 3),
                        function(x) quantile(x, 0.025))
    beta_upper <- apply(fit_fofr$bivar_func_coef[[1]], c(2, 3),
                        function(x) quantile(x, 0.975))

    # Side-by-side heatmaps: true vs estimated vs difference
    par(mfrow = c(1, 3), mar = c(4, 4, 2, 1))
    image(sindex, tindex, beta_true,
          xlab = "s (predictor domain)", ylab = "t (response domain)",
          main = expression("True " * beta(s, t)),
          col = hcl.colors(64, "Blue-Red 3"))
    image(sindex, tindex, beta_est,
          xlab = "s (predictor domain)", ylab = "t (response domain)",
          main = expression("Estimated " * hat(beta)(s, t)),
          col = hcl.colors(64, "Blue-Red 3"))
    image(sindex, tindex, beta_est - beta_true,
          xlab = "s (predictor domain)", ylab = "t (response domain)",
          main = "Difference (Est - True)",
          col = hcl.colors(64, "Blue-Red 3"))

For richer 3-d surface visualisations, use `fields::image.plot()` or `plotly::plot_ly()` with type `"surface"`.

### Scalar Coefficient Function \(\hat{\alpha}(t)\)

The estimated scalar predictor coefficient function can be plotted with pointwise credible intervals:

    alpha_est   <- apply(fit_fofr$scalar_func_coef[, 1, ], 2, mean)
    alpha_lower <- apply(fit_fofr$scalar_func_coef[, 1, ], 2,
                         function(x) quantile(x, 0.025))
    alpha_upper <- apply(fit_fofr$scalar_func_coef[, 1, ], 2,
                         function(x) quantile(x, 0.975))

    par(mfrow = c(1, 1))
    plot(tindex, alpha_true, type = "l", lwd = 2, col = "black",
         ylim = range(c(alpha_lower, alpha_upper)),
         xlab = "t (response domain)", ylab = expression(alpha(t)),
         main = "Scalar coefficient function: age")
    lines(tindex, alpha_est, col = "blue", lwd = 2)
    polygon(c(tindex, rev(tindex)),
            c(alpha_lower, rev(alpha_upper)),
            col = rgb(0, 0, 1, 0.2), border = NA)
    legend("topright",
           legend = c("Truth", "Posterior mean", "95% CI"),
           col = c("black", "blue", rgb(0, 0, 1, 0.2)),
           lwd = c(2, 2, 10), bty = "n")

### Slices of the Bivariate Coefficient

To examine \(\beta(s, t)\) at fixed values of \(s\) or \(t\), extract slices from the posterior:

    # Fix s at the midpoint of the predictor domain and plot beta(s_mid, t)
    s_mid_idx <- which.min(abs(sindex - 0.5))

    beta_slice_est   <- apply(fit_fofr$bivar_func_coef[[1]][, s_mid_idx, ], 2, mean)
    beta_slice_lower <- apply(fit_fofr$bivar_func_coef[[1]][, s_mid_idx, ], 2,
                              function(x) quantile(x, 0.025))
    beta_slice_upper <- apply(fit_fofr$bivar_func_coef[[1]][, s_mid_idx, ], 2,
                              function(x) quantile(x, 0.975))
    beta_slice_true  <- beta_true[s_mid_idx, ]

    plot(tindex, beta_slice_true, type = "l", lwd = 2, col = "black",
         ylim = range(c(beta_slice_lower, beta_slice_upper)),
         xlab = "t (response domain)",
         ylab = expression(beta(s[mid], t)),
         main = paste0("Slice at s = ", round(sindex[s_mid_idx], 2)))
    lines(tindex, beta_slice_est, col = "red", lwd = 2)
    polygon(c(tindex, rev(tindex)),
            c(beta_slice_lower, rev(beta_slice_upper)),
            col = rgb(1, 0, 0, 0.2), border = NA)
    legend("topright",
           legend = c("Truth", "Posterior mean", "95% CI"),
           col = c("black", "red", rgb(1, 0, 0, 0.2)),
           lwd = c(2, 2, 10), bty = "n")

## Numerical Summary

    # RMSE of the bivariate coefficient surface
    cat("RMSE of beta(s,t):", sqrt(mean((beta_est - beta_true)^2)), "\n")

    # RMSE of the scalar coefficient function
    cat("RMSE of alpha(t): ", sqrt(mean((alpha_est - alpha_true)^2)), "\n")

## Inspecting the Generated Stan Code

Setting `runStan = FALSE` allows you to inspect or modify the Stan code before running the model:

    # Generate Stan code without running the sampler
    fofr_code <- fofr_bayes(
      formula     = Y_mat ~ age + s(sindex, by = X_func, bs = "cr", k = 10),
      data        = dat,
      spline_type = "bs",
      spline_df   = 10,
      runStan     = FALSE
    )

    # Print the generated Stan code
    cat(fofr_code$stancode)

The generated Stan code includes all five standard blocks (`data`, `transformed data`, `parameters`, `transformed parameters`, `model`). The `parameters` block declares matrix-valued parameters for the bivariate coefficients, and the `model` block includes both \(s\)-direction and \(t\)-direction smoothness priors.

# Simulation Study: Bayesian vs Frequentist FoFR

To benchmark `fofr_bayes()` against the standard frequentist function-on-function regression fit, we ran a simulation study comparing posterior-mean prediction against the penalised tensor-product estimator implemented in `refund::pffr()`. The full simulation script is shipped as `Simulation/FoFR_Simulation_V3.R`, with a stand-alone Stan program in `Simulation/StanFoFR_Gaussian.stan`.

## Simulation Setup

Functional observations were generated from a function-on-function model without scalar predictors:

\[ Y_i(t_m) \;=\; \frac{1}{L}\sum_{l=1}^{L} W_i(s_l)\,\beta_{\text{true}}(s_l, t_m) \;+\; \epsilon_i(t_m), \qquad \epsilon_i(t_m) \stackrel{\text{iid}}{\sim} N(0, 0.5^2), \]

on uniform grids of size \(L = M = 30\) over \([0, 1]\). Functional predictors \(W_i(s)\) were generated as four-component Fourier expansions with eigenvalues \((2.5, 2.5, 2.5, 2.5)\). Two true bivariate-coefficient surfaces and three signal-strength levels were considered:

Factor | Levels
--- | ---
Sample size \(n\) | \(100,\; 200,\; 500\)
\(\beta\)-surface type | type 1: separable \(\beta_{\text{true}}(s,t) = \tau\,s\,t^2\); type 2: Gaussian bump \(\beta_{\text{true}}(s,t) = \tau\,\exp\{-5[(s-0.5)^2 + (t-0.5)^2]\}\)
Signal-strength multiplier \(\tau\) | \(1,\; 5,\; 10\)

Each cell of the \(3 \times 2 \times 3 = 18\) design was replicated approximately 500 times, giving roughly 9000 fits per method.

## Comparator Methods

- Frequentist baseline – `refund::pffr()` with a single `ff(X_func, xind = sgrid, integration = "riemann", splinepars = list(bs = "ps", k = c(10, 10)))` term and the response indexed by `yind = tgrid`. The `integration = "riemann"` option is critical here: the data-generating quadrature is left-Riemann with uniform weight \(1/L\), and matching this in `pffr()` (rather than the default Simpson rule) keeps \(\hat{\beta}\) on the same pointwise scale as \(\beta_{\text{true}}\) during recovery comparisons.
- Bayesian model – `refundBayes::fofr_bayes()` (or the equivalent standalone Stan program in `Simulation/StanFoFR_Gaussian.stan`), with `k = 10` predictor-domain cubic-regression splines, `spline_df = 10` response-domain B-splines, FPCA residual structure estimated via `refund::fpca.sc()`, 1 chain, 1000 iterations (500 warm-up).

## Performance Metric

For each replicate we draw an independent validation set of \(n_{\text{valid}} = 5000\) subjects from the same data-generating process, evaluate each method’s predicted mean response \(\widehat{\mu}_i^{\text{val}}(t)\), and compute the relative prediction MSE

\[ \text{relMSE}^{\text{pred}} \;=\; \frac{\frac{1}{n_{\text{valid}} M}\,\sum_{i,m}\bigl\{\widehat{\mu}_i^{\text{val}}(t_m) - \mu_i^{\text{val}}(t_m)\bigr\}^2}{\frac{1}{n_{\text{valid}} M}\,\sum_{i,m}\bigl\{\mu_i^{\text{val}}(t_m)\bigr\}^2}. \]

This metric is invariant to the unidentifiable additive-in-\(t\) component of \(\beta(s, t)\) (see the package’s `Simulation/FoFR_identifiability_note.md` for details), so it provides an apples-to-apples comparison even though `pffr()` includes an explicit functional intercept and `fofr_bayes()` does not.

## Results

FoFR predictive accuracy

# References

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- Crainiceanu, C. M., Goldsmith, J., Leroux, A., and Cui, E. (2024). Functional Data Analysis with R. CRC Press.
- Goldsmith, J., Zipunnikov, V., and Schrack, J. (2015). Generalized Multilevel Function-on-Scalar Regression and Principal Component Analysis. Biometrics, 71(2), 344–353.
- Ramsay, J. O. and Silverman, B. W. (2005). Functional Data Analysis, 2nd Edition. Springer.
- Ivanescu, A. E., Staicu, A.-M., Scheipl, F., and Greven, S. (2015). Penalized Function-on-Function Regression. Computational Statistics, 30(2), 539–568.
- Scheipl, F., Staicu, A.-M., and Greven, S. (2015). Functional Additive Mixed Models. Journal of Computational and Graphical Statistics, 24(2), 477–501.
- Carpenter, B., Gelman, A., Hoffman, M. D., et al. (2017). Stan: A Probabilistic Programming Language. Journal of Statistical Software, 76(1), 1–32.