IQlearning: main effect term regression
Description
Fits a linear regression of the estimated main effect term on firststage history and treatment.
Usage
1 2 3 4 5 6  learnIQ1main(object, ...)
## S3 method for class 'formula'
learnIQ1main(formula, data, treatName, intNames, s2object, ...)
## Default S3 method:
learnIQ1main(object, H1Main, A1, s1mainInts, ...)

Arguments
formula 
formula for the main effect term regression 
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 main effect term regression model 
s2object 
object of type 
object 
object of type 
H1Main 
matrix or data frame of firststage covariates to include as main effects in the linear model 
A1 
vector of firststage randomized treatments 
s1mainInts 
indices pointing to columns of H1Main that should be included as treatment interaction effects in the linear model 
... 
other arguments to be passed to 
Details
Fits a model of the form
E (H20^Tβ20  H1, A1) = H10^Tα0 + A1*H11^Tα1,
where H10 and H11 are summaries of
H1. For an object of type learnIQ1main
,
summary(object)
and plot(object)
can be used for
evaluating model fit.
Value
alphaHat0 
estimated main effect coefficients; first is the intercept 
alphaHat1 
estimated treatment interaction coefficients; first is the main effect of the firststage treatment 
s1MainFit 

mainPos 
vector of predicted values with A1=1 for all patients 
mainNeg 
vector of predicted values with A1=1 for all patients 
s1mainInts 
indicies of variables in H1Main included as
treatment
interactions in the model; same as input 
A1 
vector of firststage randomized treatments; same as
input 
Author(s)
Kristin A. Linn <kalinn@ncsu.edu>, Eric B. Laber, Leonard A. Stefanski
References
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.
See Also
learnIQ2
, summary.learnIQ1main
,
plot.learnIQ1main
Examples
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 27  ## 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 (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 (fitIQ2, s1vars, a1, c (1, 3))
fitIQ1main = learnIQ1main (~ gender + race + parent_BMI + baseline_BMI
+ A1*(gender + parent_BMI), data=bmiData, "A1", c ("gender",
"parent_BMI"), fitIQ2)
summary (fitIQ1main)
plot (fitIQ1main)
