rtree: Tree-based analysis of rare variants to analyze genomic data.

In macs: Recursive Partitioning Based Multivariate Adaptive Regression Models, Classification Trees, Survival Trees

Description

Fit a rtree model

Usage

rtree(data, method = c("entropy", "gini"), alpha = 0.01,cost = NULL)

Arguments

data	inputs data.frame. The first column of the data refers to the response and the other columns are the covariates.
method	defines the splitting rule, that is, 'entropy' for the entropy criterion and 'gini' for the Gini criterion.
alpha	determines the significance threshold by which rtree prunes the tree based on the chi-square test of independence between the distributions of the response in any two daughter nodes.
cost	defines misclassification cost of each category. It is a vector whose dimension is the same as the levels of response variable. Default value is a vector with all elements 1.

Details

The object rtree function produces can be used to run plot.ctree and forecast.ctree function.

Value

nnd	the total number of nodes in the tree.
dt	the sequence number of a left daughter node for each internal node.
pt	the sequence number of the parent node for any daughter node.
spv	the splitting variable used to split a given node.
spvl	the cut-off value of the splitting variable above.
final_counts	the table that contains the number of observations in each node.
varcatg	a numerical indicator for the category of each variable. Value '-1' points to the response variable, '1' to oridinal variables, an integer greater than 1 to a nominal variable with the number of levels equal to the integer.
nodeclass	the class membership of a terminal node which depends on the choice of the misclassification cost.
p_value	the p-value of the chi-square test performed at each internal node. It forms the basis to prune the offspring nodes of any internal node. More details in Recursive Partitioning and Applications [Zhang and Singer].
call	the call by which this object is generated.
learning.data	the data that are actually used in rtree and tarv.

References

Zhang, H. and Singer, B. (1999) Recursive partitioning in the health sciences. Springer Verlag.

See Also

plot.ctree, forecast.ctree

Examples

library(macs)
set.seed(1234)
data <- data.frame(y = sample(1:3, 1000, replace = TRUE),
                   n = sample(1:4, 1000, replace = TRUE,
                                        prob = c(0.1, 0.3, 0.2, 0.4)),
                   o1 = sample(1:50, 1000, replace = TRUE),
                   o2 = sample(1:30, 1000, replace = TRUE),
                   o3 = sample(1:10, 1000, replace = TRUE),
                   o4 = sample(1:60, 1000, replace = TRUE),
                   o5 = sample(1:20, 1000, replace = TRUE),
                   o6 = sample(1:40, 1000, replace = TRUE))
data[,2] <- as.factor(data[,2])
result <- rtree(data, method = "entropy", alpha = 0.01, cost = NULL)
plot(result)

macs documentation built on Oct. 9, 2019, 5:05 p.m.