qLearnQ1 | R Documentation |
Recommends the Q-learning estimated optimal first-stage treatment for a given stage 1 history, h1.
qLearnQ1(object, h1q)
object |
object of type |
h1q |
vector of observed first-stage main effects corresponding to the
variables in |
Use the estimated optimal first-stage decision rule from
qLearnS1()
to recommend the best stage 1 treatment for a
patient presenting with history h1q
. It is essential
that h1q
include the same variables and ordering as
H1q
. If a formula was used to fit qLearnS1()
, we
recommend checking summary(<qLearnS1 object>)
for the correct order of h1q
.
q1Pos |
estimated value of the first-stage Q-function when H1=h1 and A1=1 |
q1Neg |
estimated value of the first-stage Q-function when H1=h1 and A1=-1 |
q1opt |
estimated optimal first-stage treatment for a patient presenting with h1 |
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.
qLearnS1
, summary.qLearnS1
,
plot.qLearnS1
## 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] ## second-stage regression fitQ2 = qLearnS2 (y ~ gender + parent_BMI + month4_BMI + A2*(parent_BMI + month4_BMI), data=bmiData, "A2", c("parent_BMI", "month4_BMI")) ## first-stage regression fitQ1 = qLearnS1 (~ gender + race + parent_BMI + baseline_BMI + A1*(gender + parent_BMI), data=bmiData, "A1", c ("gender", "parent_BMI"), fitQ2) summary (fitQ1) h1q = c (1, 1, 35, 45); optQ1 = qLearnQ1 (fitQ1, h1q); optQ1$q1opt
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