learnIQ1main | R Documentation |
Fits a linear regression of the estimated main effect term on first-stage history and treatment.
learnIQ1main(object, ...) ## S3 method for class 'formula' learnIQ1main(formula, data, treatName, intNames, s2object, ...) ## Default S3 method: learnIQ1main(object, H1Main, A1, s1mainInts, ...)
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 first-stage covariates to include as main effects in the linear model |
A1 |
vector of first-stage 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 |
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.
alphaHat0 |
estimated main effect coefficients; first is the intercept |
alphaHat1 |
estimated treatment interaction coefficients; first is the main effect of the first-stage 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 first-stage 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 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.
learnIQ2
, summary.learnIQ1main
,
plot.learnIQ1main
## 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 (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)
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