Functional Dirichlet Process Spatial Temporal Aggregated Predictor in a Linear Mixed Effects Regression Model

fdp_staplmer(
  formula,
  benvo,
  weights = NULL,
  alpha_a = 1,
  alpha_b = 1,
  sigma_a = 1,
  sigma_b = 1,
  tau_a = 1,
  tau_b = 1,
  K = 5,
  iter_max = 1000,
  burn_in = 500,
  thin = 1,
  fix_alpha = FALSE,
  seed = NULL
)

Arguments

formula	Similar as for `sstap_lmer`, though fdp_staplmer is currently restricted to only one stap term.
benvo	built environment - `Benvo` - object from containing the relevant subject - Built Environment data
weights	weights for weighted regression - default is vector of ones
alpha_a	alpha gamma prior hyperparameter or alpha if fix_alpha = TRUE
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
iter_max	maximum number of iterations
burn_in	number of burn in iterations
thin	number by which to thin samples
fix_alpha	boolean value indicating whether or not to fix the concentration parameter
seed	random number generator seed will be set to default value if not by user

Value

a stapDP model object

Details

This function fits a linear mixed effects regression model in a Bayesian paradigm with improper priors assigned to the "standard" regression covariates designated in the formula argument and a Dirichlet process prior with normal-gamma base measure assigned to the stap basis function expansion using penalized splines via jagam. normal priors are placed on the latent group variables and an improper prior is placed on the correlation matrix leading to a Wishart posterior.

The concentration parameter is assigned a gamma prior with hyperparameters shape alpha_a and scale alpha_b. Precision parameters sigma_a,sigma_b, tau_a,tau_b are similar for the residual and penalties' precision, respectively.