vb_cs_fpca: Cross-sectional FoSR using Variational Bayes 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 cross-sectional data. This function estimates model parameters using a VB and estimates the residual covariance surface using FPCA.

1 2	vb_cs_fpca(formula, data = NULL, verbose = TRUE, Kt = 5, Kp = 2, alpha = 0.1, Aw = NULL, Bw = NULL, Apsi = NULL, Bpsi = NULL)

`formula`	a formula indicating the structure of the proposed model.
`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.
`verbose`	logical defaulting to `TRUE` – should updates on progress be printed?
`Kt`	number of spline basis functions used to estimate coefficient functions
`Kp`	number of FPCA basis functions to be estimated
`alpha`	tuning parameter balancing second-derivative penalty and zeroth-derivative penalty (alpha = 0 is all second-derivative penalty)
`Aw`	hyperparameter for inverse gamma controlling variance of spline terms for population-level effects
`Bw`	hyperparameter for inverse gamma controlling variance of spline terms for population-level effects
`Apsi`	hyperparameter for inverse gamma controlling variance of spline terms for FPC effects
`Bpsi`	hyperparameter for inverse gamma controlling variance of spline terms for FPC effects