earl {DynTxRegime} | R Documentation |

Efficient Augmentation and Relaxation Learning

earl( ..., moPropen, moMain, moCont, data, response, txName, regime, iter = 0L, fSet = NULL, lambdas = 0.5, cvFolds = 0L, surrogate = "hinge", kernel = "linear", kparam = NULL, 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 treatment. See ?moPropen for details. |

`moMain` |
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 main effects of the outcome. See ?modelObj for details. |

`moCont` |
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 contrasts of the outcome. See ?modelObj for details. |

`data` |
A data frame of the covariates and tx histories |

`response` |
The response variable. |

`txName` |
A character object.
The column header of |

`regime` |
A formula object or a list of formula objects. The covariates to be included in classification. If a list is provided, this specifies that there is an underlying subset structure – fSet must then be defined. |

`iter` |
Maximum number of iterations for outcome regression |

`fSet` |
A function or NULL defining subset structure |

`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. |

`surrogate` |
The surrogate 0-1 loss function must be one of logit, exp, hinge, sqhinge, huber |

`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 |

`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. |

an EARL object

Ying-Qi Zhao, Eric Laber, Sumona Saha and Bruce E. Sands (2016+) Efficient augmentation and relaxation learning for treatment regimes using observational data

Other statistical methods:
`bowl()`

,
`iqLearn`

,
`optimalClass()`

,
`optimalSeq()`

,
`owl()`

,
`qLearn()`

,
`rwl()`

Other single decision point methods:
`optimalClass()`

,
`optimalSeq()`

,
`owl()`

,
`qLearn()`

,
`rwl()`

Other weighted learning methods:
`bowl()`

,
`owl()`

,
`rwl()`

# 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')) # outcome model moMain <- buildModelObj(model = ~parentBMI+month4BMI, solver.method = 'lm') moCont <- buildModelObj(model = ~parentBMI+month4BMI, solver.method = 'lm') fitEARL <- earl(moPropen = moPropen, moMain = moMain, moCont = moCont, data = bmiData, response = y12, txName = 'A2', regime = ~ parentBMI + month4BMI, surrogate = 'logit', kernel = 'poly', kparam = 2) ##Available methods # Coefficients of the regression objects coef(fitEARL) # Description of method used to obtain object DTRstep(fitEARL) # Estimated value of the optimal treatment regime for training set estimator(fitEARL) # Value object returned by regression methods fitObject(fitEARL) # Summary of optimization routine optimObj(fitEARL) # Estimated optimal treatment for training data optTx(fitEARL) # Estimated optimal treatment for new data optTx(fitEARL, bmiData) # Value object returned by outcome regression method outcome(fitEARL) # Plots if defined by regression methods dev.new() par(mfrow = c(2,4)) plot(fitEARL) plot(fitEARL, suppress = TRUE) # Value object returned by propensity score regression method propen(fitEARL) # Parameter estimates for decision function regimeCoef(fitEARL) # Show main results of method show(fitEARL) # Show summary results of method summary(fitEARL)

[Package *DynTxRegime* version 4.9 Index]