
This function implements multiple stage O-learning (with improved single stage O-learing) to find optimal DTR by backward induction.
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)
is either a matrix shared among all stages; or list of feature matrices, where feature matrices from different stages can have different dimensions.
a list of K, each element A[[i]]
is the treatment assignment vector for stage i.
a list of K, each element R[[i]]
is the outcome vector for stage i.
sample size
number of stages
a list of K, the i'th element is the randomization probability at stage i
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
The choice of kernel for Improved O-learning, default is 'linear'
, can also be 'rbf'
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)
is grid of tuning parameter for wsvm
, which cross validation was run to choose from. the default is 2^(-2:2)
number of folds in cross validation for Olearning_Single
.
The rounding error for computing bias in wsvm
a list of models of class 'linearcl'
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.
# 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)
# }
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