learnIQ2 | R Documentation |
Fits a linear regression of the response on second-stage history and treatment to estimate the optimal second-stage decision rule.
learnIQ2(H2, ...) ## S3 method for class 'formula' learnIQ2(formula, data, treatName, intNames, ...) ## Default S3 method: learnIQ2(H2, Y, A2, s2ints, ...)
formula |
stage 2 regression formula |
data |
data frame containing variables used in |
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 |
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.
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 |
|
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 |
A2 |
vector of second-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.
summary.learnIQ2
, plot.learnIQ2
## 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.