NumBotMaxDepthX {BayesTreePrior} R Documentation

## 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, α \in [0,1). beta power parameter of the tree prior, beta ≥q 0. X data.frame of the design matrix. depth depth of the current node, depth ≥q 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.

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)
}


[Package BayesTreePrior version 1.0.1 Index]