| qLearnQ2 | R Documentation | 
Recommends the estimated optimal second-stage treatment for a given
stage 2 history, h2. This is the same as IQ2.
qLearnQ2(object, h2)
object  | 
 object of type   | 
h2  | 
 vector of observed second-stage main effects corresponding to the
variables in   | 
Use the estimated optimal second-stage decision rule from
qLearnS2() to recommend the best stage 2 treatment for a
patient presenting with history h2. It is essential
that h2 include the same variables and ordering as
H2. If a formula was used to fit qLearnS2(), we
recommend checking summary(qLearnS2) for the correct order of h2.
q2Pos  | 
 estimated value of the second-stage Q-function when H2=h2 and A2=1  | 
q2Neg  | 
 estimated value of the second-stage Q-function when H2=h2 and A2=-1  | 
q2opt  | 
 estimated optimal second-stage treatment for a patient presenting with h2  | 
Kristin A. Linn <kalinn@ncsu.edu>, Eric B. Laber, Leonard A. Stefanski
Linn, K. A., Laber, E. B., Stefanski, L. A. (2015) "iqLearn: Interactive Q-Learning in R", Journal of Statistical Software, 64(1), 1–25.
Laber, E. B., Linn, K. A., and Stefanski, L. A. (2014) "Interactive model building for Q-learning", Biometrika, 101(4), 831-847.
qLearnS2, summary.qLearnS2,
plot.qLearnS2
## load in two-stage BMI data
data (bmiData)
bmiData$A1[which (bmiData$A1=="MR")] = 1
bmiData$A1[which (bmiData$A1=="CD")] = -1
bmiData$A2[which (bmiData$A2=="MR")] = 1
bmiData$A2[which (bmiData$A2=="CD")] = -1
bmiData$A1 = as.numeric (bmiData$A1)
bmiData$A2 = as.numeric (bmiData$A2)
s1vars = bmiData[,1:4]
s2vars = bmiData[,c (1, 3, 5)]
a1 = bmiData[,7]
a2 = bmiData[,8]
## define response y to be the negative 12 month change in BMI from
## baseline 
y = -(bmiData[,6] - bmiData[,4])/bmiData[,4]
fitQ2 = qLearnS2 (y ~ gender + parent_BMI + month4_BMI +
  A2*(parent_BMI + month4_BMI), data=bmiData, "A2", c("parent_BMI",
  "month4_BMI"))                                     
summary (fitQ2)
h2 = c (1, 30, 45)
optQ2 = qLearnQ2 (fitQ2, h2)
optQ2$q2opt
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