qLearn | R Documentation |

Performs a single step of the Q-Learning algorithm.
If an object of class `QLearn`

is passed through input response,
it is assumed that the `QLearn`

object is the value object returned
from the preceding step of the Q-Learning algorithm, and
the value fit by the regression is taken from the `QLearn`

object.
If a vector is passed through input response, it is assumed that the
call if for the first step in the Q-Learning algorithm, and
models are fit using the provided response.

qLearn( ..., moMain, moCont, data, response, txName, fSet = NULL, iter = 0L, verbose = TRUE )

`...` |
ignored. Provided to require named inputs. |

`moMain` |
An object of class modelObj or a list of objects of class
modelObjSubset, which define the models and R methods to be used to
obtain parameter estimates and predictions for the main effects component
of the outcome regression. |

`moCont` |
An object of class modelObj or a list of objects of class
modelObjSubset, which define the models and R methods to be used to
obtain parameter estimates and predictions for the contrasts component
of the outcome regression. |

`data` |
A data frame of covariates and treatment history. |

`response` |
A response vector or object of class QLearn from a previous Q-Learning step. |

`txName` |
A character string giving column header of treatment variable in data |

`fSet` |
NULL or a function. This argument allows the user to specify the subset of treatment options available to a patient. See ?fSet for details of allowed structure |

`iter` |
An integer. See ?iter for details |

`verbose` |
A logical. If TRUE, screen prints are generated. |

An object of class QLearn-class

Other statistical methods:
`bowl()`

,
`earl()`

,
`iqLearn`

,
`optimalClass()`

,
`optimalSeq()`

,
`owl()`

,
`rwl()`

Other multiple decision point methods:
`bowl()`

,
`iqLearn`

,
`optimalClass()`

,
`optimalSeq()`

Other single decision point methods:
`earl()`

,
`optimalClass()`

,
`optimalSeq()`

,
`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] # outcome model moMain <- buildModelObj(model = ~parentBMI+month4BMI, solver.method = 'lm') moCont <- buildModelObj(model = ~race + parentBMI+month4BMI, solver.method = 'lm') #### Second-Stage Analysis fitSS <- qLearn(moMain = moMain, moCont = moCont, data = bmiData, response = y12, txName = 'A2') ##Available methods # Coefficients of the outcome regression objects 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 outcome regression method fitObject(fitSS) # Estimated optimal treatment and decision functions for training data optTx(fitSS) # Estimated optimal treatment and decision functions for new data optTx(fitSS, bmiData) # Value object returned by outcome regression method outcome(fitSS) # Plots if defined by outcome regression method dev.new() par(mfrow = c(2,4)) plot(fitSS) plot(fitSS, suppress = TRUE) # Show main results of method show(fitSS) # Show summary results of method summary(fitSS) #### First-stage Analysis # outcome model moMain <- buildModelObj(model = ~parentBMI+baselineBMI, solver.method = 'lm') moCont <- buildModelObj(model = ~race + parentBMI+baselineBMI, solver.method = 'lm') fitFS <- qLearn(moMain = moMain, moCont = moCont, data = bmiData, response = fitSS, txName = 'A1') ##Available methods for fitFS are as shown above for fitSS

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