Description Usage Arguments Details Value Author(s) References See Also Examples
Fits a linear regression of the response on second-stage history and treatment to estimate the optimal second-stage decision rule.
1 2 3 4 5 6 |
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
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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.