learnIQ1cm: IQ-learning: contrast function mean regression

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/learnIQ1cm.R

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

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, ...)

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 <[email protected]>, 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

 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 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 29, 2017, 11:26 a.m.