Fitting function for function-on-scalar regression for cross-sectional data. This function estimates model parameters using a Gibbs sampler and estimates the residual covariance surface using a Wishart prior.
gibbs_cs_wish(
formula,
Kt = 5,
data = NULL,
verbose = TRUE,
N.iter = 5000,
N.burn = 1000,
alpha = 0.1,
min.iter = 10,
max.iter = 50,
Aw = NULL,
Bw = NULL,
v = NULL,
SEED = NULL
)a formula indicating the structure of the proposed model.
number of spline basis functions used to estimate coefficient functions
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.
logical defaulting to TRUE -- should updates on progress be printed?
number of iterations used in the Gibbs sampler
number of iterations discarded as burn-in
tuning parameter balancing second-derivative penalty and zeroth-derivative penalty (alpha = 0 is all second-derivative penalty)
minimum number of iterations
maximum number of iterations
hyperparameter for inverse gamma controlling variance of spline terms for population-level effects
hyperparameter for inverse gamma controlling variance of spline terms for population-level effects
hyperparameter for inverse Wishart prior on residual covariance
seed value to start the sampler; ensures reproducibility
Jeff Goldsmith ajg2202@cumc.columbia.edu
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.