owl: Outcome Weighted Learning

Description

Outcome Weighted Learning

Usage

owl(
  ...,
  moPropen,
  data,
  reward,
  txName,
  regime,
  response,
  lambdas = 2,
  cvFolds = 0L,
  kernel = "linear",
  kparam = NULL,
  surrogate = "hinge",
  verbose = 2L
)

Value

an OWL object

Arguments

...: Used primarily to require named input. However, inputs for the optimization methods can be sent through the ellipsis. If surrogate is hinge, the optimization method is kernlab::ipop(). For all other surrogates, stats::optim() is used.
moPropen: An object of class modelObj, which defines the model and R methods to be used to obtain parameter estimates and predictions for the propensity for treatment. See ?moPropen for details.
data: A data frame of the covariates and tx histories
reward: The response vector
txName: A character object. The column header of data that corresponds to the tx covariate
regime: A formula object or a character vector. The covariates to be included in classification
response: A numeric vector. The reward. Allows for naming convention followed in most DynTxRegime methods.
lambdas: A numeric object or a numeric vector object giving the penalty tuning parameter. If more than 1 is provided, the finite set of values to be considered in the cross-validation algorithm
cvFolds: If cross-validation is to be used to select the tuning parameters, the number of folds.
kernel: A character object. must be one of {"linear", "poly", "radial"}
kparam: A numeric object of NULL. If kernel = linear, kparam is ignored. If kernel = poly, kparam is the degree of the polynomial If kernel = radial, kparam is the inverse bandwidth of the kernel. If a vector of bandwidth parameters is given, cross-validation will be used to select the parameter
surrogate: The surrogate 0-1 loss function must be one of logit, exp, hinge, sqhinge, huber
verbose: An integer or logical. If 0, no screen prints are generated. If 1, screen prints are generated with the exception of optimization results obtained in iterative algorithm. If 2, all screen prints are generated.

References

Yingqi Zhao, Donglin Zeng, A. John Rush, Michael R. Kosorok (2012) Estimated individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association, 107(409): 1106-1118. PMCID: 3636816

Examples

Run this code

 
# Load and process data set
data(bmiData)

# define the negative 12 month change in BMI from baseline
y12 <- -100*(bmiData[,6L] - bmiData[,4L])/bmiData[,4L]

# propensity model
moPropen <- buildModelObj(model = ~parentBMI+month4BMI,
                          solver.method = 'glm',
                          solver.args = list('family'='binomial'),
                          predict.method = 'predict.glm',
                          predict.args = list(type='response'))

fitOWL <- owl(moPropen = moPropen,
              data = bmiData, reward = y12,  txName = 'A2', 
              regime = ~ parentBMI + month4BMI,
              surrogate = 'hinge', kernel = 'linear', kparam = NULL)

##Available methods

  # Coefficients of the propensity score regression
  coef(fitOWL)

  # Description of method used to obtain object
  DTRstep(fitOWL)

  # Estimated value of the optimal treatment regime for training set
  estimator(fitOWL)

  # Value object returned by propensity score regression method
  fitObject(fitOWL)

  # Summary of optimization routine
  optimObj(fitOWL)

  # Estimated optimal treatment for training data
  optTx(fitOWL)

  # Estimated optimal treatment for new data
  optTx(fitOWL, bmiData)

  # Plots if defined by propensity regression method
  dev.new()
  par(mfrow = c(2,4))

  plot(fitOWL)
  plot(fitOWL, suppress = TRUE)

  # Value object returned by propensity score regression method
  propen(fitOWL)

  # Parameter estimates for decision function
  regimeCoef(fitOWL)

  # Show main results of method
  show(fitOWL)

  # Show summary results of method
  summary(fitOWL)