abclass_propscore: Estimate Propensity Score by the Angle-Based Classifiers

Description

A wrap function to estimate the propensity score by the multi-category angle-based large-margin classifiers.

Usage

abclass_propscore(
  x,
  treatment,
  loss = c("logistic", "boost", "hinge.boost", "lum"),
  penalty = c("glasso", "gscad", "gmcp", "lasso", "scad", "mcp", "cmcp", "gel",
    "mellowmax", "mellowmcp"),
  weights = NULL,
  offset = NULL,
  intercept = TRUE,
  control = list(),
  tuning = c("et", "cv_1se", "cv_min"),
  ...
)

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.
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.
tuning: A character vector specifying the tuning method. This argument will be ignored if a single lambda is specified through control.
...: Other arguments passed to the corresponding methods.