NumBotMaxDepthX {BayesTreePrior}

Number of bottom nodes and depth in the general case (Case #4).

Description

Generate a tree and returns the number of bottom nodes and depth in the general case (Case #4).

Usage

NumBotMaxDepthX(alpha, beta, X, depth = 0, minpart = 1, pvars = NULL,
  MIA = FALSE, missingdummy = FALSE)

Arguments

alpha

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

beta

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

X

data.frame of the design matrix.

depth

depth of the current node, depth \geq 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.

Value

Returns a vector containing the number of bottom nodes and depth

References

Twala, B. E. T. H., Jones, M. C., & Hand, D. J. (2008). Good methods for coping with missing data in decision trees. Pattern Recognition Letters, 29(7), 950-956.

See Also

NumBotMaxDepth_inf, NumBotMaxDepth

Examples

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 = NumBotMaxDepthX(.95,.5, data.frame(X), minpart=5)
    X_dummies = is.na(X) + 0
    results2 = NumBotMaxDepthX(.95,.5, data.frame(cbind(X,X_dummies)), minpart=5, MIA=TRUE, 
    missingdummy=TRUE)
}