Efficient Augmentation and Relaxation Learning
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 data that corresponds to the tx covariate |
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()
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.