Description Usage Arguments Details Value Author(s) References See Also Examples
Regresses the predicted future outcome maximized over a2 on first-stage history and treatment to estimate the optimal first-stage decision rule using Q-learning.
1 2 3 4 5 6 7 |
formula |
right-hand sided stage 1 regression formula |
data |
data frame containing variables used in |
treatName |
character string indicating the stage 1 treatment name |
intNames |
vector of characters indicating the names of the variables that interact with the stage 1 treatment in the regression model |
qS2object |
object of type |
object |
object of type |
H1q |
matrix or data frame of first-stage covariates to include as main effects in the linear model |
A1 |
vector of first-stage randomized treatments |
s1ints |
indices pointing to columns of H1q that should be included as treatment interaction effects in the linear model |
... |
other arguments to be passed to |
Fits a model of the form
E (Ytilde | H1, A1) = H10^Tβ10 + A1*H11^Tβ11,
where H10 and H11 are summaries of
H1. For an object of type qLearnS1
,
summary(object)
and plot(object)
can be used for
evaluating model fit.
betaHat10 |
estimated main effect coeffients, beginning with the intercept |
betaHat11 |
estimated treatment interaction coefficients, beginning with the main effect of treatment |
optA1 |
vector of estimated optimal first-stage treatments for the patients in the training data |
s1Fit |
|
s1ints |
indices of variables in |
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.
summary.qLearnS2
, plot.qLearnS2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ## 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
fitQ2 = qLearnS2 (s2vars, y, a2, s2ints)
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 (fitQ2, s1vars, a1, c(3,4))
fitQ1 = qLearnS1 (~ gender + race + parent_BMI + baseline_BMI +
A1*(gender + parent_BMI), data=bmiData, "A1", c ("gender",
"parent_BMI"), fitQ2)
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