qhat_rangerlogit: 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 ensemble of ranger and logit.

Description

Estimate marginal and joint distribution of lists j and k using ensemble of ranger and logit.

Usage

qhat_rangerlogit(
  List.train,
  List.test,
  K = 2,
  j = 1,
  k = 2,
  margin = 0.005,
  ...
)

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.
...	Any extra arguments passed into the function.

Value

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

References

Marvin N. Wright, Andreas Ziegler (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1), 1-17. doi:10.18637/jss.v077.i01

Polley, Eric C. and van der Laan, Mark J., (May 2010) Super Learner In Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 266. https://biostats.bepress.com/ucbbiostat/paper266

Examples

## Not run: 
qhat = qhat_ranger(List.train = List.train, List.test = List.test, margin = 0.005)
q1 = qhat$q1
q2 = qhat$q2
q12 = qhat$q12

## End(Not run)

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