Usage
gibbs_mult_wish(formula, Kt = 5, data = NULL, verbose = TRUE,
N.iter = 5000, N.burn = 1000, alpha = 0.1, Az = NULL, Bz = NULL,
Aw = NULL, Bw = NULL, v = NULL, SEED = NULL)
Arguments
formula
a formula indicating the structure of the proposed model.
Kt
number of spline basis functions used to estimate coefficient functions
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?
N.iter
number of iterations used in the Gibbs sampler
N.burn
number of iterations discarded as burn-in
alpha
tuning parameter balancing second-derivative penalty and
zeroth-derivative penalty (alpha = 0 is all second-derivative penalty)
Az
hyperparameter for inverse gamma controlling variance of spline terms
for subject-level effects
Bz
hyperparameter for inverse gamma controlling variance of spline terms
for subject-level effects
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
v
hyperparameter for inverse Wishart prior on residual covariance
SEED
seed value to start the sampler; ensures reproducibility