Split the given dataset into balanced training and validation sets (within a pre-specified tolerance) Balanced means 1) The ratio of treated and controls is maintained in the training and validation sets 2) The covariate distributions are balanced between the training and validation sets

Usage

balancemean.split(
  y,
  trt,
  x.cate,
  x.ps,
  minPS = 0.01,
  maxPS = 0.99,
  train.prop = 3/4,
  error.max = 0.1,
  max.iter = 5000
)

Arguments

y: Observed outcome; vector of size n (observations)
trt: Treatment received; vector of size n with treatment coded as 0/1
x.cate: Matrix of p.cate baseline covariates; dimension n by p.cate (covariates in the outcome model)
x.ps: Matrix of p.ps baseline covariates (plus a leading column of 1 for the intercept); dimension n by p.ps + 1 (covariates in the propensity score model plus intercept)
minPS: A numerical value (in `[0, 1]`) below which estimated propensity scores should be truncated. Default is 0.01.
maxPS: A numerical value (in `(0, 1]`) above which estimated propensity scores should be truncated. Must be strictly greater than minPS. Default is 0.99.
train.prop: A numerical value (in `(0, 1)`) indicating the proportion of total data used for training. Default is 3/4.
error.max: A numerical value > 0 indicating the tolerance (maximum value of error) for the largest standardized absolute difference in the covariate distributions or in the doubly robust estimated rate ratios between the training and validation sets. This is used to define a balanced training-validation splitting. Default is 0.1.
max.iter: A positive integer value indicating the maximum number of iterations when searching for a balanced training-validation split. Default is 5,000.

Value

A list of 10 objects, 5 training and 5 validation of y, trt, x.cate, x.ps, time: y.train - observed outcome in the training set; vector of size m (observations in the training set) trt.train - treatment received in the training set; vector of size m coded as 0/1 x.cate.train - baseline covariates for the outcome model in the training set; matrix of dimension m by p.cate x.ps.train - baseline covariates (plus intercept) for the propensity score model in the training set; matrix of dimension m by p.ps + 1 y.valid - observed outcome in the validation set; vector of size n-m trt.valid - treatment received in the validation set; vector of size n-m coded as 0/1 x.cate.valid - baseline covariates for the outcome model in the validation set; matrix of dimension n-m by p.cate x.ps.valid - baseline covariates (plus intercept) for the propensity score model in the validation set; matrix of dimension n-m by p.ps + 1 bestid.valid - id for the validation set by the best split; vector of size n-m