A Step of the Q-Learning Algorithm
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 fitSSPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.