Functional Dirichlet Process Spatial Temporal Aggregated Predictor Linear Model Fit

Source: R/fdp_staplm.R

Functional Dirichlet Process Spatial Temporal Aggregated Predictor Linear Model Fit

fdp_staplm.fit(
  y,
  Z,
  X,
  S,
  weights = rep(1, length(y)),
  alpha_a = 1,
  alpha_b = 1,
  sigma_a = 1,
  sigma_b = 1,
  tau_a = 1,
  tau_b = 1,
  K = 5,
  iter_max,
  burn_in,
  thin = 1,
  fix_alpha = FALSE,
  seed = NULL
)

Arguments

y

vector of outcomes
Z

design matrix
X

stap design matrix
S

list of penalty matrices from jagam
weights

weights for weighted regression - default is vector of ones
alpha_a

alpha gamma prior hyperparameter
alpha_b

alpha gamma prior hyperparameter
sigma_a

precision gamma prior hyperparameter
sigma_b

precision gamma prior hyperparameter
tau_a

penalty parameters gamma prior hyperparameter
tau_b

penalty parameters gamma prior hyperparameter
K

truncation number for DP mixture components
iter_max

maximum number of iterations
burn_in

number of iterations to burn-in
thin

number by which to thin samples
fix_alpha

boolean value
seed

random number generator seed will be set to default value if not by user