hebart | R Documentation |
This function runs a BCART model and returns the tree and other results obtained in the last iteration of the MCMC
hebart( formula, dataset, iter = 100, group_variable = "group", pars, min_u = 0, max_u = 20, prior_k1 = TRUE, num.trees = 5, sample_k1 = TRUE, burn_in = 50, alpha_grow = 0.9, beta_grow = 0.5, ... )
formula |
The model formula |
dataset |
The data to be used in the modeling |
iter |
The number of MCMC iterations |
group_variable |
The grouping variable |
pars |
The hyperparameters set/list |
min_u |
Integer representing the lower interval of the Uniform distribution used to sample k1 |
max_u |
Integer representing the upper interval of the |
prior_k1 |
Logical to decide whether or not use a prior for k1 Uniform distribution used to sample k1 |
num.trees |
The number of trees |
sample_k1 |
Logical to decide whether to sample_k1 or not |
burn_in |
The number of burn-in iterations |
alpha_grow |
Number between 0 and 1 used in the growing probability calculation |
beta_grow |
Number between 0 and 1 used in the growing probability calculation |
... |
Other parameters |
Priors used ———————————————————- y_ij ~ Normal(m_j, tau^-1) tau ~ Gamma(alpha, beta) mu ~ Normal(0, tau_mu = k2*tau^-1) mu_j ~ Normal(mu, k1*tau^-1) ———————————————————————-
A list containing: the sampled values of tau and k1, the final trees
Bruna Wundervald, brunadaviesw@gmail.com.
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