learnIQ1cm: IQ-learning: contrast function mean regression

In iqLearn: Interactive Q-Learning

Description

Estimates the mean of the contrast function by fitting a linear regression of the estimated contrast function term on first-stage history and treatment.

Usage

learnIQ1cm(object, ...)

## S3 method for class 'formula'
learnIQ1cm(formula, data, treatName, intNames, s2object, ...)
## Default S3 method:
learnIQ1cm(object, H1CMean, A1, s1cmInts, ...)

Arguments

formula	formula for the contrast function mean regression
data	data frame containing variables used in formula
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 learnIQ2
object	object of type learnIQ2
H1CMean	matrix or data frame of first-stage covariates to include as main effects in the linear model
A1	vector of first-stage 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 lm()

Details

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.

Value

betaHat10	estimated main effect coefficients; first is the intercept
betaHat11	estimated treatment interaction coefficients; first is the main effect of the first-stage treatment
s1cmFit	lm() object of the contrast mean regression fit
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 s1cmInts
A1	vector of first-stage randomized treatments; same as input A1

Author(s)

Kristin A. Linn <[e-mail]>, Eric B. Laber, Leonard A. Stefanski

References

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.

See Also

learnIQ2, summary.learnIQ1cm, plot.learnIQ1cm

Examples

## 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"))
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)

iqLearn documentation built on May 2, 2019, 6:44 a.m.