moml: Multi-Category Outcome-Weighted Margin-Based Learning (MOML)

In abclass: Angle-Based Classification

Multi-Category Outcome-Weighted Margin-Based Learning (MOML)

Description

Performs the outcome-weighted margin-based learning for multicategory treatments proposed by Zhang, et al. (2020).

Usage

moml(
  x,
  treatment,
  reward,
  propensity_score,
  loss = c("logistic", "boost", "hinge.boost", "lum"),
  penalty = c("glasso", "lasso"),
  weights = NULL,
  offset = NULL,
  intercept = TRUE,
  control = moml.control(),
  ...
)

moml.control(...)

Arguments

x	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument intercept to TRUE to include an intercept term instead of adding an all-one column to x.
treatment	The assigned treatments represented by a character, integer, numeric, or factor vector.
reward	A numeric vector representing the rewards. It is assumed that a larger reward is more desirable.
propensity_score	A numeric vector taking values between 0 and 1 representing the propensity score.
loss	A character value specifying the loss function. The available options are "logistic" for the logistic deviance loss, "boost" for the exponential loss approximating Boosting machines, "hinge.boost" for hybrid of SVM and AdaBoost machine, and "lum" for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.
penalty	A character vector specifying the name of the penalty.
weights	A numeric vector for nonnegative observation weights. Equal observation weights are used by default.
offset	An optional numeric matrix for offsets of the decision functions.
intercept	A logical value indicating if an intercept should be considered in the model. The default value is TRUE and the intercept is excluded from regularization.
control	A list of control parameters. See abclass.control() for details.
...	Other arguments passed to the control function, which calls the abclass.control() internally.

References

Zhang, C., Chen, J., Fu, H., He, X., Zhao, Y., & Liu, Y. (2020). Multicategory outcome weighted margin-based learning for estimating individualized treatment rules. Statistica Sinica, 30, 1857–1879.

abclass documentation built on Nov. 5, 2025, 6:27 p.m.