Penalized Functional Dirichlet Process Linear Regression with N observations

Penalized Functional Dirichlet Process Linear Regression with N observations

stappDP_fit(
  y,
  Z,
  X,
  S,
  w,
  alpha_a,
  alpha_b,
  sigma_a,
  sigma_b,
  tau_a,
  tau_b,
  K,
  num_penalties,
  iter_max,
  burn_in,
  thin,
  seed,
  num_posterior_samples,
  fix_alpha
)

Arguments

y	a vector of continuous outcomes
Z	a matrix of population level confounders
X	a matrix of spatial temporal aggregated predictors
S	penalty matrix for stap parameters
w	a vector of weights for weighted regression
alpha_a	alpha gamma prior shape hyperparameter
alpha_b	alpha gamma prior scale hyperparameter
sigma_a	precision gamma prior shape hyperparameter
sigma_b	precision gamma prior scale hyperparameter
tau_a	penalty gamma prior shape hyperparameter
tau_b	penalty gamma prior scale hyperparameter
K	truncation number
num_penalties	number of penalty matrices accounted for in S
iter_max	maximum number of iterations
burn_in	number of burn in iterations
thin	number by which to thin samples
seed	rng initializer
num_posterior_samples	total number of posterior samples
fix_alpha	boolean value that determines whether or not to fix alpha in sampler