BayesTreePriorOrthogonal: Simulation of the tree prior in the case where we have one...

In AlexiaJM/BayesTreePrior: Bayesian Tree Prior Simulation

Description

Generate n_{iter} trees from the prior distribution in the case where we have one variable with a finite number of observations (Case #3).

Usage

BayesTreePriorOrthogonal(alpha, beta, n_obs, n_iter = 500)

Arguments

alpha	base parameter of the tree prior, α \in [0,1).
beta	power parameter of the tree prior, beta ≥q 0.
n_obs	number of unique observations, n_{obs}>1.
n_iter	number of trees to generate, n_{iter}>0.

Value

Returns a list containing, in the following order: the mean number of bottom nodes, the standard deviation of the number of bottom nodes, the mean of the depth, the standard deviation of the depth and a data.frame of vectors (b_i,d_i), where b_i is the number of bottom nodes and d_i is the depth of the ith generated tree (i=1, … ,n_{iter}).

See Also

BayesTreePriorOrthogonalInf, BayesTreePriorNotOrthogonal

Examples

results1 = BayesTreePriorOrthogonal(.95,.5, 100)
results2 = BayesTreePriorOrthogonal(.95,.5, 250)

AlexiaJM/BayesTreePrior documentation built on May 5, 2019, 4:53 a.m.