gibbs_mult_fpca: Multilevel FoSR using a Gibbs sampler and FPCA
In jeff-goldsmith/BayesFoSR: Bayesian Function-on-Scalar Regression

Description Usage Arguments Author(s) References

Fitting function for function-on-scalar regression for longitudinal data. This function estimates model parameters using a Gibbs sampler and estimates the residual covariance surface using FPCA.

1 2	gibbs_mult_fpca(formula, Kt = 5, Kp = 2, data = NULL, N.iter = 5000, N.burn = 1000, sig2.me = 0.01, alpha = 0.1, SEED = NULL)

`formula`	a formula indicating the structure of the proposed model.
`Kt`	number of spline basis functions used to estimate coefficient functions
`Kp`	number of FPCA basis functions to be estimated
`data`	an optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function is called.
`N.iter`	number of iterations used in the Gibbs sampler
`N.burn`	number of iterations discarded as burn-in
`sig2.me`	starting value for measurement error variance
`alpha`	tuning parameter balancing second-derivative penalty and zeroth-derivative penalty (alpha = 0 is all second-derivative penalty)
`seed`	seed value to start the sampler; ensures reproducibility
`verbose`	logical defaulting to `TRUE` – should updates on progress be printed?