optimalClass {DynTxRegime} | R Documentation |

Classification Perspective

```
optimalClass(
...,
moPropen,
moMain,
moCont,
moClass,
data,
response,
txName,
iter = 0L,
fSet = NULL,
verbose = TRUE
)
```

`...` |
Included to require named inputs |

`moPropen` |
An object of class modelObj, which defines the models 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, which defines the models and R methods to be used to obtain parameter estimates and predictions for for the main effects component of the outcome regression. See ?modelObj for details. NULL is an appropriate value. |

`moCont` |
An object of class modelObj, which defines the models and R methods to be used to obtain parameter estimates and predictions for for the contrasts component of the outcome regression. See ?modelObj for details. NULL is an appropriate value. |

`moClass` |
An object of class modelObj, which defines the models and R methods to be used to obtain parameter estimates and predictions for the classification. See ?modelObj for details. |

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

`response` |
The response vector |

`txName` |
An character giving the column header of the column in data that contains the tx covariate. |

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

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

`verbose` |
A logical If FALSE, screen prints are suppressed. |

an object of class OptimalClass

Baqun Zhang, Anastasios A. Tsiatis, Marie Davidian, Min Zhang and Eric B. Laber. "Estimating optimal tx regimes from a classification perspective." Stat 2012; 1: 103-114.

Note that this method is a single decision point, binary treatment method. For multiple decision points, can be called repeatedly.

Other statistical methods:
`bowl()`

,
`earl()`

,
`iqLearn`

,
`optimalSeq()`

,
`owl()`

,
`qLearn()`

,
`rwl()`

Other single decision point methods:
`earl()`

,
`optimalSeq()`

,
`owl()`

,
`qLearn()`

,
`rwl()`

Other multiple decision point methods:
`bowl()`

,
`iqLearn`

,
`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 propensity for treatment model and methods.
moPropen <- buildModelObj(model = ~ 1,
solver.method = 'glm',
solver.args = list('family'='binomial'),
predict.method = 'predict.glm',
predict.args = list(type='response'))
# classification model
library(rpart)
moClass <- buildModelObj(model = ~parentBMI+month4BMI+race+gender,
solver.method = 'rpart',
solver.args = list(method="class"),
predict.args = list(type='class'))
#### Second-Stage Analysis using IPW
fitSS_IPW <- optimalClass(moPropen = moPropen,
moClass = moClass,
data = bmiData, response = y12, txName = 'A2')
# outcome model
moMain <- buildModelObj(model = ~parentBMI+month4BMI,
solver.method = 'lm')
moCont <- buildModelObj(model = ~race + parentBMI+month4BMI,
solver.method = 'lm')
#### Second-Stage Analysis using AIPW
fitSS_AIPW <- optimalClass(moPropen = moPropen,
moMain = moMain, moCont = moCont,
moClass = moClass,
data = bmiData, response = y12, txName = 'A2')
##Available methods
# Retrieve the classification regression object
classif(object = fitSS_AIPW)
# Coefficients of the outcome regression objects
coef(object = fitSS_AIPW)
# Description of method used to obtain object
DTRstep(object = fitSS_AIPW)
# Estimated value of the optimal treatment regime for training set
estimator(x = fitSS_AIPW)
# Value object returned by outcome regression method
fitObject(object = fitSS_AIPW)
# Estimated optimal treatment and decision functions for training data
optTx(x = fitSS_AIPW)
# Estimated optimal treatment and decision functions for new data
optTx(x = fitSS_AIPW, newdata = bmiData)
# Value object returned by outcome regression method
outcome(object = fitSS_AIPW)
outcome(object = fitSS_IPW)
# Plots if defined by outcome regression method
dev.new()
par(mfrow = c(2,4))
plot(x = fitSS_AIPW)
plot(x = fitSS_AIPW, suppress = TRUE)
# Retrieve the value object returned by propensity regression method
propen(object = fitSS_AIPW)
# Show main results of method
show(object = fitSS_AIPW)
# Show summary results of method
summary(object = fitSS_AIPW)
#### First-stage Analysis using AIPW
# Define the propensity for treatment model and methods.
moPropen <- buildModelObj(model = ~ 1,
solver.method = 'glm',
solver.args = list('family'='binomial'),
predict.method = 'predict.glm',
predict.args = list(type='response'))
# classification model
moClass <- buildModelObj(model = ~parentBMI+baselineBMI+race+gender,
solver.method = 'rpart',
solver.args = list(method="class"),
predict.args = list(type='class'))
# outcome model
moMain <- buildModelObj(model = ~parentBMI+baselineBMI,
solver.method = 'lm')
moCont <- buildModelObj(model = ~race + parentBMI+baselineBMI,
solver.method = 'lm')
fitFS_AIPW <- optimalClass(moPropen = moPropen,
moMain = moMain, moCont = moCont,
moClass = moClass,
data = bmiData, response = fitSS_AIPW,
txName = 'A1')
##Available methods for fitFS_AIPW are as shown above for fitSS_AIPW
```

[Package *DynTxRegime* version 4.15 Index]