bowl | R Documentation |

Function performs a single step of the bowl method. Multiple decision points can be analyzed by repeated calls, as is done for qLearn() and optimalClass().

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

`...` |
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 dfoptim::hjk(). For all other surrogates, stats::optim() is used. |

`moPropen` |
An object of class modelObj or modelObjSubset, which defines the model and R methods to be used to obtain parameter estimates and predictions for the propensity for tx. 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 |

`regime` |
A formula object or a list of formula objects. The covariates to be included in the decision function/kernel. If a list is provided, this specifies that there is an underlying subset structure – fSet must then be defined. For subsets, the name of each element of the list must correspond to the name of a subset. If a regime is to be estimated using multiple subsets combined, each subset must be included in the name and separated by a comma (no spaces). |

`response` |
A numeric vector. The same as reward above. Allows for naming convention followed in most DynTxRegime methods. |

`BOWLObj` |
NULL or |

`lambdas` |
A numeric object or a numeric vector object giving the penalty tuning parameter(s). If more than 1 is provided, the set of tuning parameter values to be considered in the cross-validation algorithm (note that cvFolds must be positive in this case). |

`cvFolds` |
If cross-validation is to be used to select the tuning parameters and/or kernel parameters, the number of folds. |

`kernel` |
A character object. Must be one of {'linear', 'poly', 'radial'} |

`kparam` |
A numeric object. |

`fSet` |
A function or NULL defining subset structure. See ?fSet for details. |

`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. |

a `BOWL-class`

object

Yingqi Zhao, Donglin Zeng, Eric B. Laber, Michael R. Kosorok (2015) New statistical learning methods for estimating optimal dynamic treatment regimes. Journal of the American Statistical Association, 110:510, 583–598.

Other statistical methods:
`earl()`

,
`iqLearn`

,
`optimalClass()`

,
`optimalSeq()`

,
`owl()`

,
`qLearn()`

,
`rwl()`

Other weighted learning methods:
`earl()`

,
`owl()`

,
`rwl()`

Other multiple decision point methods:
`iqLearn`

,
`optimalClass()`

,
`optimalSeq()`

,
`qLearn()`

# 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] # define the negative 4 month change in BMI from baseline y4 <- -100*(bmiData[,5L] - bmiData[,4L])/bmiData[,4L] # reward for second stage rewardSS <- y12 - y4 #### Second-stage regression # Constant propensity model moPropen <- buildModelObj(model = ~1, solver.method = 'glm', solver.args = list('family'='binomial'), predict.method = 'predict.glm', predict.args = list(type='response')) fitSS <- bowl(moPropen = moPropen, data = bmiData, reward = rewardSS, txName = 'A2', regime = ~ parentBMI + month4BMI) ##Available methods # Coefficients of the propensity score regression coef(fitSS) # Description of method used to obtain object DTRstep(fitSS) # Estimated value of the optimal treatment regime for training set estimator(fitSS) # Value object returned by propensity score regression method fitObject(fitSS) # Summary of optimization routine optimObj(fitSS) # Estimated optimal treatment for training data optTx(fitSS) # Estimated optimal treatment for new data optTx(fitSS, bmiData) # Plots if defined by propensity regression method dev.new() par(mfrow = c(2,4)) plot(fitSS) plot(fitSS, suppress = TRUE) # Value object returned by propensity score regression method propen(fitSS) # Parameter estimates for decision function regimeCoef(fitSS) # Show main results of method show(fitSS) # Show summary results of method summary(fitSS) #### First-stage regression # Constant propensity model fitFS <- bowl(moPropen = moPropen, data = bmiData, reward = y4, txName = 'A1', regime = ~ gender + parentBMI, BOWLObj = fitSS, lambdas = c(0.5, 1.0), cvFolds = 4L) ##Available methods for fitFS are as shown above for fitSS # Results of the cross-validation cvInfo(fitFS)

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