qhat_sl {drpop} | R Documentation |
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 |
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
## End(Not run)