node_dipm: Panel-Generator for Visualization of A Precision Medicine...

In dipm: Depth Importance in Precision Medicine (DIPM) Method

Panel-Generator for Visualization of A Precision Medicine Tree

Description

This function provides a new plot method for dipm and spmtree. It visualizes stratified treatment groups through boxplots for a continuous outcome and survival plots for a survival outcome, respectively.

Usage

node_dipm(obj, ...)

Arguments

obj	A party tree object returned from either the dipm() or spmtree() function
...	Arguments passed on to plotfun

Details

This function visualizes the precision medicine trees proposed in Chen and Zhang (2020a, b).

Value

No return value, called for plot

References

Chen, V., Li, C., and Zhang, H. (2022). dipm: an R package implementing the Depth Importance in Precision Medicine (DIPM) tree and Forest-based method. Bioinformatics Advances, 2(1), vbac041.

Chen, V. and Zhang, H. (2020). Depth importance in precision medicine (DIPM): a tree and forest based method. In Contemporary Experimental Design, Multivariate Analysis and Data Mining, 243-259.

Chen, V. and Zhang, H. (2022). Depth importance in precision medicine (DIPM): A tree-and forest-based method for right-censored survival outcomes. Biostatistics 23(1), 157-172.

Seibold, H., Zeileis, A., and Hothorn, T. (2019). model4you: An R package for personalised treatment effect estimation. Journal of Open Research Software 7(1).

Hothorn, T. and Zeileis, A. (2015). partykit: a modular toolkit for recursive partytioning in R. The Journal of Machine Learning Research 16(1), 3905-3909.

See Also

dipm, spmtree

Examples

#' #
# ... an example with a continuous outcome variable
#     and two treatment groups
#

N = 100
set.seed(123)


# generate binary treatments
treatment = rbinom(N, 1, 0.5)

# generate candidate split variables
X1 = rnorm(n = N, mean = 0, sd = 1)
X2 = rnorm(n = N, mean = 0, sd = 1)
X3 = rnorm(n = N, mean = 0, sd = 1)
X4 = rnorm(n = N, mean = 0, sd = 1)
X5 = rnorm(n = N, mean = 0, sd = 1)
X = cbind(X1, X2, X3, X4, X5)
colnames(X) = paste0("X", 1:5)

# generate continuous outcome variable
calculateLink = function(X, treatment){

    ((X[, 1] <= 0) & (X[, 2] <= 0)) *
        (25 * (1 - treatment) + 8 * treatment) + 

    ((X[, 1] <= 0) & (X[, 2] > 0)) *
        (18 * (1 - treatment) + 20 * treatment) +

    ((X[,1 ] > 0) & (X[, 3] <= 0)) *
        (20 * (1 - treatment) + 18 * treatment) + 

    ((X[,1] > 0) & (X[,3] > 0)) *
        (8 * (1 - treatment) + 25 * treatment)
}

Link = calculateLink(X, treatment)
Y = rnorm(N, mean = Link, sd = 1)

# combine variables in a data frame
data = data.frame(X, Y, treatment)

# fit a dipm classification tree
tree = dipm(Y ~ treatment | ., data, mtry = 1, maxdepth = 3) 
plot(tree, terminal_panel = node_dipm)
            

                                    

dipm documentation built on June 16, 2025, 9:06 a.m.