tune_theta {tehtuner}

Estimate the penalty parameter for Step 2 of Virtual Twins

Description

Permutes data under the null hypothesis of a constant treatment effect and calculates the MNPP on each permuted data set. The 1 - alpha quantile of the distribution is taken.

Usage

tune_theta(
  data,
  Trt,
  Y,
  zbar,
  step1,
  step2,
  threshold,
  alpha0,
  p_reps,
  parallel,
  ...
)

Arguments

data

a data frame containing a response, binary treatment indicators, and covariates.

Trt

a string specifying the name of the column of data contains the treatment indicators.

Y

a string specifying the name of the column of data contains the response.

zbar

the estimated marginal treatment effect

step1

character strings specifying the Step 1 model. Supports either "lasso", "mars", "randomforest", or "superlearner".

step2

a character string specifying the Step 2 model. Supports "lasso", "rtree", "classtree", or "ctree".

threshold

for "step2 = 'classtree'" only. The value against which to test if the estimated individual treatment effect from Step 1 is higher (TRUE) or lower (FALSE).

alpha0

the nominal Type I error rate.

p_reps

the number of permutations to run.

parallel

Should the loop over replications be parallelized? If FALSE, then no, if TRUE, then yes. Note that running in parallel requires a parallel backend that must be registered before performing the computation. See the foreach documentation for more details.

...

additional arguments to the Step 1 model call.

Value

the estimated penalty parameter