Recommends the estimated optimal secondstage treatment for a given stage 2 history, h2.
1  IQ2(object, h2)

object 
object of type 
h2 
vector of observed secondstage main effects corresponding to the
variables in 
Use the estimated optimal secondstage decision rule from
learnIQ2()
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 learnIQ2()
, we
recommend checking summary(<learnIQ2 object>)
for the correct order of h2
.
q2Pos 
estimated value of the secondstage Qfunction when H2=h2 and A2=1 
q2Neg 
estimated value of the secondstage Qfunction when H2=h2 and A2=1 
q2opt 
estimated optimal secondstage 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 QLearning 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 Qlearning", Biometrika, 101(4), 831847.
learnIQ2
, summary.learnIQ2
,
plot.learnIQ2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  ## load in twostage 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]
fitIQ2 = learnIQ2 (y ~ gender + parent_BMI + month4_BMI +
A2*(parent_BMI + month4_BMI), data=bmiData, "A2", c("parent_BMI",
"month4_BMI"))
summary (fitIQ2)
## new patient
h2 = c (1, 30, 45)
optIQ2 = IQ2 (fitIQ2, h2)
optIQ2$q2opt

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