Multiple responses subgroup identification using 'GUIDE' 'Gi' option for tree building
MrSFit(
dataframe,
role,
bestK = 1,
bootNum = 0L,
alpha = 0.05,
maxDepth = 5,
minTrt = 5,
minData = max(c(minTrt * maxDepth, NROW(Y)/20)),
batchNum = 1L,
CVFolds = 10L,
CVSE = 0,
faster = FALSE,
display = FALSE,
treeName = paste0("tree_", format(Sys.time(), "%m%d"), ".yaml"),
nodeName = paste0("node_", format(Sys.time(), "%m%d"), ".txt"),
bootName = paste0("boot_", format(Sys.time(), "%m%d"), ".txt"),
impName = paste0("imp_", format(Sys.time(), "%m%d"), ".txt"),
writeTo = FALSE,
remove = TRUE
)The data frame used for subgroup identification in a data.frame format.
The data frame should contains covariates, treatment assignment and outcomes. The order of variables does not matter.
role follows 'GUIDE' role. role should be a vector,
with same length as dataframe's column. The role serves for providing usage of each column in dataframe.
In current implementation, we have following available roles.
Covariates roles
c Categorical variable used for splitting only.
f Numerical variable used only for fitting the regression models in the nodes of tree. It will not be used for splitting the nodes.
h Numerical variable always held in fitting the regression models in the nodes of tree.
n Numerical variable used both for splitting the nodes and fitting the node regression model.
s Numerical variable only used for splitting the node. It will not be used for fitting the regression model.
x Exclude variable. Variable will not be used in tree building.
Outcome role
d Dependent variable. If there is only one d variable, function will do single response subgroup identification.
Treatment role
r Categorical tReatment variable used only for fitting the linear models in the nodes of tree. It is not used for splitting the nodes.
number of covariates in the regression model
bootstrap number
desire alpha levels for confidence interval with respect to treatment parameters
maximum tree depth
minimum treatment and placebo sample in each node
minimum sample in each node
related with exhaustive search for numerical split variable
cross validation times
cross validation SE
related with tree split searching
Whether display tree in the end
yaml file for save the tree
file same for each node
file save bootstrap calibrate alpha
important variable file name
debug option reserve for author...
whether to remove extra files
An object of class "guide"
Tree structure result.
Predicted node of each observation.
A raw importance score, can used MrSImp for more accurate result.
Categorical features level mapping.
Treatment assignment level mapping.
Number of outcomes.
Number of treatment assignment levels.
Role used for data frame.
Variable names.
Numerical variable names.
Categorical variable names.
Treatment assignment variable name.
A map from node id to node information.
Treatment level mapping.
Current tree setting.
Treatment effect summary.
This function uses 'GUIDE' Gi option for tree building, it can provide subgroup identification tree and confidence intervals of treatment effect based on bootstrap calibration.
'Gi' option is testing the interaction between covariate \(x_i\) and treatment assignment \(z\). With in each tree node \(t\), if \(x_i\) is a continuous variable, the function will discretize it into four parts as \(h_i\) based on sample quartiles. If \(x_i\) is a categorical variable, function will set \(h_i\) = \(x_i\). If \(x_i\) contains missing value, the function will add missing as a new level into \(H_i\). Then, we test the full model against the main effect model.
$$H_0 = \beta_0 + \sum\limits_{i=2}^{H}\beta_{hi}I(h_i = i) + \sum\limits_{j=2}^{G}\beta_{zj}I(Z_j = j)$$ $$H_A = \beta_0 + \sum\limits_{i=2, j=2}\beta_{ij}I(h_i = i, Z_j = j)$$
Then choose the most significant \(x_i\). The details algorithm can be found in Loh, W.-Y. and Zhou, P. (2020).
The bootstrap confidence interval of treatment can be fond in Loh et al. (2019).
Loh, W.-Y. and Zhou, P. (2020). The GUIDE approach to subgroup identification. In Design and Analysis of Subgroups with Biopharmaceutical Applications, N. Ting, J. C. Cappelleri, S. Ho, and D.-G. Chen (Eds.) Springer, in press.
Loh, W.-Y., Man, M. and Wang, S. (2019). Subgroups from regression trees with adjustment for prognostic effects and post-selection inference. Statistics in Medicine, vol. 38, 545-557. doi:10.1002/sim.7677 http://pages.stat.wisc.edu/~loh/treeprogs/guide/sm19.pdf
# NOT RUN {
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 <- MrSFit(dataframe = train, role = role)
printTree(mrsobj)
# }
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