BayesTreePriorNotOrthogonal: Simulation of the tree prior in the general case (Case #4).
In AlexiaJM/BayesTreePrior: Bayesian Tree Prior Simulation

Description Usage Arguments Value See Also Examples

Generate n_{iter} trees from the prior distribution in the general case (Case #4).

1 2	BayesTreePriorNotOrthogonal(alpha, beta, X, n_iter = 500, minpart = 1, pvars = NULL, MIA = FALSE, missingdummy = FALSE)

`alpha`	base parameter of the tree prior, α \in [0,1).
`beta`	power parameter of the tree prior, β ≥q 0.
`X`	data.frame of the design matrix.
`n_iter`	number of trees to generate, n_{iter}>0.
`minpart`	the minimum number of observations required in one of the child to be able to split, minpart>0.
`pvars`	vector of probabilities for the choices of variables to split (Will automatically be normalized so that the sum equal to 1). It must be twice as large as the number of variables when missingdummy is TRUE.
`MIA`	set to TRUE if you want Missing Incorporated in Attributes (MIA) imputation to be used.
`missingdummy`	set to TRUE if you have dummy coded the NAs.

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}).

BayesTreePriorOrthogonalInf, BayesTreePriorOrthogonal

if (requireNamespace("MASS", quietly = TRUE)) {
    x1 = MASS::mcycle$times
    x1[sample(1:length(x1), 20)] <- NA
    x2= MASS::mcycle$accel
    x2[sample(1:length(x2), 20)] <- NA
    X = cbind(x1, x2)
    results1 = BayesTreePriorNotOrthogonal(.95,.5, data.frame(X), minpart=5)
    X_dummies = is.na(X) + 0
    results2 = BayesTreePriorNotOrthogonal(.95,.5, data.frame(cbind(X,X_dummies)), minpart=5, 
    MIA=TRUE, missingdummy=TRUE)
}