BayesTreePriorOrthogonalInf {BayesTreePrior}

Simulation of the tree prior in the unrealistic case where we assume that the number of variables and possible splits are infinite (therefore P(T) is not dependent on the design matrix X) (Case #2).

Description

Generate n_{iter} trees from the prior distribution in the unrealistic case where we assume that the number of variables and possible splits are infinite (therefore P(T) is not dependent on the design matrix X) (Case #2).

Usage

BayesTreePriorOrthogonalInf(alpha, beta, n_iter = 500)

Arguments

alpha

base parameter of the tree prior, \alpha \in [0,1).

beta

power parameter of the tree prior, beta \geq 0.

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, \ldots ,n_{iter}).

See Also

BayesTreePriorOrthogonal, BayesTreePriorNotOrthogonal

Examples

results = BayesTreePriorOrthogonalInf(.95,.5)