DTRlearn (version 1.3)

Olearning: Multiple stage Improved Olearning

Description

This function implements multiple stage O-learning (with improved single stage O-learing) to find optimal DTR by backward induction.

Usage

Olearning(X,AA,RR,n,K,pi, pentype="lasso",kernel="linear",
sigma=c(0.03,0.05,0.07),clinear = 2^(-2:2), m = 4, e = 1e-05)

Arguments

X

is either a matrix shared among all stages; or list of feature matrices, where feature matrices from different stages can have different dimensions.

AA

a list of K, each element A[[i]] is the treatment assignment vector for stage i.

RR

a list of K, each element R[[i]] is the outcome vector for stage i.

n

sample size

K

number of stages

pi

a list of K, the i'th element is the randomization probability at stage i

pentype

the type of regression used to take residual, 'lasso' is the default, using lasso regression; 'LSE' is the ordianry least square regression. as in the function wsvm

kernel

The choice of kernel for Improved O-learning, default is 'linear', can also be 'rbf'

sigma

if kernel='rbf', sigma is the grid of tuning parameter for 'rbf' kernal to run cross validation to choose from, the default is (0.03, 0.05, 0.07)

clinear

is grid of tuning parameter for wsvm, which cross validation was run to choose from. the default is 2^(-2:2)

m

number of folds in cross validation for Olearning_Single.

e

The rounding error for computing bias in wsvm

Value

models

a list of models of class 'linearcl'

References

Liu et al. (2015). Under double-blinded review.

Zhao, Y., Zeng, D., Rush, A. J., & Kosorok, M. R. (2012). Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association, 107(499), 1106-1118.

Zhao, Y. Q., Zeng, D., Laber, E. B., & Kosorok, M. R. (2014). New statistical learning methods for estimating optimal dynamic treatment regimes. Journal of the American Statistical Association, (just-accepted), 00-00.

See Also

Olearning_Single

Examples

# NOT RUN {
n_cluster=10
pinfo=10
pnoise=20
example2=make_2classification(n_cluster,pinfo,pnoise,200)
test=make_2classification(n_cluster,pinfo,pnoise,200,example2$centroids)
pi=list()
pi[[2]]=pi[[1]]=rep(1,200)
modelO=Olearning(example2$X,example2$A,example2$R,200,2,pi)
# }