vb_cs_fpca: Cross-sectional FoSR using Variational Bayes and FPCA

Description

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.

Usage

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

Arguments

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
argvals: not currently implemented

Author

Jeff Goldsmith ajg2202@cumc.columbia.edu

References

Goldsmith, J., Kitago, T. (2016). Assessing Systematic Effects of Stroke on Motor Control using Hierarchical Function-on-Scalar Regression. Journal of the Royal Statistical Society: Series C, 65 215-236.