learnIQ1 | R Documentation |
Estimates the optimal first-stage decision rule using IQ-learning.
learnIQ1(mainObj, cmObj, sigObj, dens)
mainObj |
object of type |
cmObj |
object of type |
sigObj |
object of type |
dens |
either "norm" or "nonpar"; density estimator to use for the conditional density of the contrast function |
If dens="norm"
the normal location-scale density estimator is
used, otherwise when dens="nonpar"
the empirical density
estimator is used.
optA1 |
vector of estimated optimal first-stage treatment for the patients in the training data |
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.
learnIQ1main
, learnIQ1cm
, learnIQ1var
## 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] s2ints = c (2, 3) ## second-stage regression fitIQ2 = learnIQ2 (y ~ gender + parent_BMI + month4_BMI + A2*(parent_BMI + month4_BMI), data=bmiData, "A2", c("parent_BMI", "month4_BMI")) ## model conditional expected value of main effect term fitIQ1main = learnIQ1main (~ gender + race + parent_BMI + baseline_BMI + A1*(gender + parent_BMI), data=bmiData, "A1", c ("gender", "parent_BMI"), fitIQ2) ## model conditional mean of contrast function fitIQ1cm = learnIQ1cm (~ gender + race + parent_BMI + baseline_BMI + A1*(gender + parent_BMI + baseline_BMI), data=bmiData, "A1", c ("gender", "parent_BMI", "baseline_BMI"), fitIQ2) ## variance modeling fitIQ1var = learnIQ1var (~ gender + race + parent_BMI + baseline_BMI + A1*(parent_BMI), data=bmiData, "A1", c ("parent_BMI"), "hetero", fitIQ1cm) ## get optimal first-stage txts fitIQLearn = learnIQ1 (fitIQ1main, fitIQ1cm, fitIQ1var, "nonpar")
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