MrSImp: Variable importance for predictive variables

In MrSGUIDE: Multiple Responses Subgroup Identification using 'GUIDE' Algorithm

Description

Variable importance in subgroup identification for predictive variables.

Usage

MrSImp(
  dataframe,
  role,
  B = 100,
  bestK = 1,
  maxDepth = 5,
  minTrt = 5,
  minData = max(c(minTrt * maxDepth, NROW(Y)/20)),
  batchNum = 1L,
  faster = FALSE,
  display = FALSE,
  treeName = paste0("tree_", format(Sys.time(), "%m%d"), ".yaml"),
  nodeName = paste0("node_", format(Sys.time(), "%m%d"), ".txt"),
  impName = paste0("imp_", format(Sys.time(), "%m%d"), ".txt")
)

Arguments

dataframe	train data frame
role	role follows GUIDE role
B	bootstrap number default = 100
bestK	number of covariates in the regression model
maxDepth	maximum tree depth
minTrt	minimum treatment and placebo sample in each node
minData	minimum sample in each node
batchNum	related with exhaustive search for numerical split variable
faster	related with tree split searching
display	Whether display tree in the end
treeName	yaml file for save the tree
nodeName	file same for each node
impName	important variable file name

Details

MrSGUIDE variable importance

Value

A list contains importance score variable names and roles

imp	Importance score data frame
role	Role for each variable
Settings	Settings used to build the tree

Author(s)

Peigen Zhou

Examples

library(MrSGUIDE)
set.seed(1234)

N = 200
np = 3

numX <- matrix(rnorm(N * np), N, np) ## numerical features
gender <- sample(c('Male', 'Female'), N, replace = TRUE)
country <- sample(c('US', 'UK', 'China', 'Japan'), N, replace = TRUE)

z <- sample(c(0, 1), N, replace = TRUE) # Binary treatment assignment

y1 <- numX[, 1] + 1 * z * (gender == 'Female') + rnorm(N)
y2 <- numX[, 2] + 2 * z * (gender == 'Female') + rnorm(N)

train <- data.frame(numX, gender, country, z, y1, y2)
role <- c(rep('n', 3), 'c', 'c', 'r', 'd', 'd')

mrsobj <- MrSImp(dataframe = train, role = role, B = 10)
mrsobj$imp

MrSGUIDE documentation built on Oct. 23, 2020, 5:19 p.m.