Fit a Bayesian nonparametric factorial ANOVA model with Gaussian kernel

Fit a Bayesian nonparametric factorial ANOVA model with Gaussian kernel

Usage

anova_bnp_normal(
  y,
  X,
  iter = 4000L,
  warmup = 2000L,
  seed = 1L,
  n = 50L,
  rho = 1,
  a = 1,
  b = 1,
  a0 = 2,
  lambda0 = 1,
  mu0 = 0,
  b0 = 1,
  lb = mean(y) - 3 * sd(y),
  ub = mean(y) + 3 * sd(y),
  standardize_y = FALSE
)

Arguments

y

a continuous response vector.

X

a design matrix (full of integer covariates).

iter

the total number of mcmc iterations.

warmup

the number of warmup mcmc iterations.

seed

the seed for random number generation.

n

the number of points \(y_0\) for computing \(p(y_0 | y)\). The final grid is conformed by n equispaced points from lb to ub, see the arguments lb and ub.

rho

the hyperparameter \(\rho\).

a

the hyperparameter \(a\).

b

the hyperparameter \(b\).

a0

the hyperparameter \(a_0\).

lambda0

the hyperparameter \(\lambda_0\).

mu0

the hyperparameter \(\mu_0\).

b0

the hyperparameter \(b_0\).

lb

the lower bound of the prediction grid.

ub

the upper bound of the prediction grid.

standardize_y

Select TRUE to internally standardize the data, fit the model, and present the results in the original scale.

Value

An object of class anova_bnp_model.