Description Usage Arguments Details Value Author(s) References See Also Examples
Estimates the mean of the contrast function by fitting a linear regression of the estimated contrast function term on firststage history and treatment.
1 2 3 4 5 6  learnIQ1cm(object, ...)
## S3 method for class 'formula'
learnIQ1cm(formula, data, treatName, intNames, s2object, ...)
## Default S3 method:
learnIQ1cm(object, H1CMean, A1, s1cmInts, ...)

formula 
formula for the contrast function mean 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 contrast function mean regression model 
s2object 
object of type 
object 
object of type 
H1CMean 
matrix or data frame of firststage covariates to include as main effects in the linear model 
A1 
vector of firststage randomized treatments 
s1cmInts 
indices pointing to columns of H1CMean 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 (H21^Tβ21  H1, A1) = H11^Tβ10 + A1*H11^Tβ11,
where H10 and H11 are summaries of
H1. Though a slight abuse of notation, these summaries are
not required to be the same as H10 and H11 in
the main effect term regression or the variance model. For an object of type learnIQ1cm
,
summary(object)
and plot(object)
can be used for
evaluating model fit.
betaHat10 
estimated main effect coefficients; first is the intercept 
betaHat11 
estimated treatment interaction coefficients; first is the main effect of the firststage treatment 
s1cmFit 

cmeanResids 
residuals from the regression 
cmPos 
vector of predicted values with A1=1 for all patients 
cmNeg 
vector of predicted values with A1=1 for all patients 
s1cmInts 
indicies of variables in H1CMean included as
treatment
interactions in the model; same as input 
A1 
vector of firststage randomized treatments; same as
input 
Kristin A. Linn <[email protected]>, 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.
learnIQ2
, summary.learnIQ1cm
,
plot.learnIQ1cm
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  ## 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"))
fitIQ1cm = learnIQ1cm (~ gender + race + parent_BMI + baseline_BMI +
A1*(gender + parent_BMI + baseline_BMI), data=bmiData, "A1",
c ("gender", "parent_BMI", "baseline_BMI"), fitIQ2)
summary (fitIQ1cm)
plot (fitIQ1cm)

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