IQ-learning: second-stage regression

Description

Fits a linear regression of the response on second-stage history and treatment to estimate the optimal second-stage decision rule.

Usage

1
2
3
4
5
6
learnIQ2(H2, ...)

## S3 method for class 'formula'
learnIQ2(formula, data, treatName, intNames, ...)
## Default S3 method:
learnIQ2(H2, Y, A2, s2ints, ...)

Arguments

formula

stage 2 regression formula

data

data frame containing variables used in formula

treatName

character string indicating the stage 2 treatment name

intNames

vector of characters indicating the names of the variables that interact with the stage 2 treatment in the regression model

H2

matrix or data frame of second-stage covariates to include as main effects in the linear model

Y

response vector

A2

vector of second-stage randomized treatments

s2ints

indices pointing to columns of H2 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 (Y | H2, A2) = H20^Tβ20 + A2*H21^Tβ21,

where H20 and H21 are summaries of H2. For an object of type learnIQ2, summary(object) and plot(object) can be used for evaluating model fit.

Value

betaHat20

estimated main effect coefficients; first is the intercept

betaHat21

estimated treatment interaction coefficients; first is the main effect of the second-stage treatment

s2Fit

lm() object of the second-stage regression fit

optA2

vector of estimated optimal second-stage treatments for the patients in the training data

main

estimated main effect vector, H20^T\hat{β20}

contrast

estimated contrast function vector, H21^T\hat{β21}

s2ints

indicies of variables in H2 included as treatment interactions in the model; same as input s2ints

A2

vector of second-stage randomized treatments; same as input A2

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 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

summary.learnIQ2, plot.learnIQ2

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
## 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 (s2vars, y, a2, s2ints)
fitIQ2 = learnIQ2 (y ~ gender + parent_BMI + month4_BMI +
  A2*(parent_BMI + month4_BMI), data=bmiData, "A2", c("parent_BMI",
                                  "month4_BMI"))
                                   
summary (fitIQ2)
plot (fitIQ2)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.