qhat_sl: Estimate marginal and joint distribution of lists j and k...

In mqnjqrid/crctmle: Efficient and Doubly Robust Population Size Estimation

Estimate marginal and joint distribution of lists j and k using super learner.

Description

Estimate marginal and joint distribution of lists j and k using super learner.

Usage

qhat_sl(
  List.train,
  List.test,
  K = 2,
  j = 1,
  k = 2,
  margin = 0.005,
  sl.lib = c("SL.glm", "SL.gam", "SL.glm.interaction", "SL.ranger", "SL.glmnet"),
  num_cores = NA,
  ...
)

Arguments

List.train	The training data matrix used to estimate the distibution functions.
List.test	The data matrix on which the estimator function is applied.
K	The number of lists in the data.
j	The first list that is conditionally independent.
k	The second list that is conditionally independent.
margin	The minimum value the estimates can attain to bound them away from zero.
sl.lib	The functions from the SuperLearner library to be used for model fitting. See SuperLearner::listWrappers().
num_cores	The number of cores to be used for paralellization in Super Learner.
...	Any extra arguments passed into the function.

Value

A list of the marginal and joint distribution probabilities q1, q2 and q12.

References

Eric Polley, Erin LeDell, Chris Kennedy and Mark van der Laan (2021). SuperLearner: Super Learner Prediction. R package version 2.0-28. https://CRAN.R-project.org/package=SuperLearner

van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2008) Super Learner, Statistical Applications of Genetics and Molecular Biology, 6, article 25.

Examples

## Not run: 
qhat = qhat_sl(List.train = List.train, List.test = List.test, margin = 0.005, num_cores = 1)
q1 = qhat$q1
q2 = qhat$q2
q12 = qhat$q12
# One can specify the number of cores to be used for parallel computing
qhat = qhat_sl(List.train = List.train, List.test = List.test, margin = 0.005, num_cores = 2)
q1 = qhat$q1
q2 = qhat$q2
q12 = qhat$q12

## End(Not run)

mqnjqrid/crctmle documentation built on June 3, 2022, 9:38 p.m.