tmle_parallel: Modify TMLE to support parallel computation for g and Q.

In ck37/ckTools: Chris Kennedy's R Toolkit

Modify TMLE to support parallel computation for g and Q.

Description

This is needed to use Savio or any multicore system effectively.

Another benefit is that the SuperLearner objects for the Q and g estimation are saved. This allows one to examine the risk estimates for example.

Usage

tmle_parallel(
  Y,
  A,
  W,
  family,
  g.SL.library,
  Q.SL.library,
  id = 1:length(Y),
  verbose = F,
  V = 5,
  sl_fn = SuperLearner::SuperLearner,
  cvsl_fn = SuperLearner::CV.SuperLearner,
  cvQinit = F,
  conserve_memory = T,
  ...
)

Arguments

Y	Outcome
A	Treatment indicator
W	Covariates
family	Gaussian or binomial
g.SL.library	SL library for estimating g
Q.SL.library	SL library for estimating Q
id	Optional list of subject-specific ids.
verbose	If TRUE outputs additional information during execution.
V	Number of cross-validation folds to use when estimating g and Q. Defaults to 5 as tmle package does.
sl_fn	SuperLearner function to use for estimation of g and possibly Q. By default this uses the normal SuperLearner function which is sequential. Other options would be to pass in mcSuperLearner, snowSuperLearner, or CV.SuperLearner. For functions that require additional arguments (e.g. the cluster argument of for snowSuperLearner) one should create a new function that overloads the call and sets that argument. This is what setup_parallel_tmle() does.
cvsl_fn	CV.SuperLearner equivalent, can be used for estimating Q.
cvQinit	If T, estimate Q using cvsl_fn, otherwise use sl_fn.
conserve_memory	If T, remove the fitLibrary elements to save memory after we have done the relevant prediction.
...	Remaining arguments are passed through to tmle::tmle().

See Also

setup_parallel_tmle

ck37/ckTools documentation built on April 29, 2023, 11:47 p.m.