HTT: Hypothesis Testing Tree

In HTT: Hypothesis Testing Tree

Hypothesis Testing Tree

Description

Fit a hypothesis testing tree.

Usage

HTT(formula, data, method, distance, controls = htt_control(...), ...)

Arguments

formula	a symbolic description of the model to be fit.
data	a data frame containing the variables in the model.
method	"regression" or "classification". If method is missing then the routine tries to make an intelligent guess. If Y is factor, then method = "classification". If Y is numeric vector or numeric matrix, then method = "classification".
distance	If distance is missing, then Euclidean distance with exponent alpha is used for regression tree, 0-1 distance is used for classification tree. Otherwise, use the distance as the distance matrix of Y.
controls	a list of options that control details of the HTT algorithm. See htt_control.
...	arguments passed to htt_control.

Details

Hypothesis testing trees examines the distribution difference over two child nodes by the binary partitioning in a hypothesis testing framework. At each split, it finds the maximum distribution difference over all possible binary partitions, the test statistic is based on generalized energy distance. The permutation test is used to estimate the p-value of the hypothesis testing.

Value

An object of class htt. See htt.object.

Author(s)

Jiaqi Hu

See Also

htt_control, print.htt, plot.htt, predict.htt

Examples

## regression
data("Boston", package = "MASS")
Bostonhtt <- HTT(medv ~ . , data = Boston, controls = htt_control(R = 99))
plot(Bostonhtt)
mean((Boston$medv - predict(Bostonhtt))^2)

## classification
irishtt <- HTT(Species ~., data = iris)
plot(irishtt)
mean(iris$Species == predict(irishtt))

HTT documentation built on March 31, 2023, 5:40 p.m.