Fits a linear regression of the response on secondstage history and treatment to estimate the optimal secondstage decision rule.
1 2 3 4 5 6 
formula 
stage 2 regression formula 
data 
data frame containing variables used in 
treatName 
character string indicating the stage 2 treatment name 
intNames 
vector of characters indicating the names of the variables that interact with the stage 2 treatment in the regression model 
H2 
matrix or data frame of secondstage covariates to include as main effects in the linear model 
Y 
response vector 
A2 
vector of secondstage randomized treatments 
s2ints 
indices pointing to columns of H2 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 (Y  H2, A2) = H20^Tβ20 + A2*H21^Tβ21,
where H20 and H21 are summaries of
H2. For an object of type learnIQ2
,
summary(object)
and plot(object)
can be used for
evaluating model fit.
betaHat20 
estimated main effect coefficients; first is the intercept 
betaHat21 
estimated treatment interaction coefficients; first is the main effect of the secondstage treatment 
s2Fit 

optA2 
vector of estimated optimal secondstage treatments for the patients in the training data 
main 
estimated main effect vector, H20^T\hat{β20} 
contrast 
estimated contrast function vector, H21^T\hat{β21} 
s2ints 
indicies of variables in H2 included as treatment
interactions in the model; same as input 
A2 
vector of secondstage randomized treatments; same as
input 
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.
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 24  ## 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]
s2ints = c (2, 3)
## secondstage regression
fitIQ2 = learnIQ2 (s2vars, y, a2, s2ints)
fitIQ2 = learnIQ2 (y ~ gender + parent_BMI + month4_BMI +
A2*(parent_BMI + month4_BMI), data=bmiData, "A2", c("parent_BMI",
"month4_BMI"))
summary (fitIQ2)
plot (fitIQ2)

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