tmle {drpop}

Returns the targeted maximum likelihood estimates for the nuisance functions

Description

Returns the targeted maximum likelihood estimates for the nuisance functions

Usage

tmle(
  datmat,
  iter = 250,
  margin = 0.005,
  stop_margin = 0.005,
  twolist = FALSE,
  K = 2,
  ...
)

Arguments

datmat

The data frame containing columns yj, yk, yjk, q10, q02 and q12.

iter

An integer denoting the maximum number of iterations allowed for targeted maximum likelihood method. Default value is 100.

margin

The minimum value the estimates can attain to bound them away from zero.

stop_margin

The minimum value the estimates can attain to bound them away from zero.

twolist

The logical value of whether targeted maximum likelihood algorithm fits only two modes when K = 2.

K

The number of lists in the original data.

...

Any extra arguments passed into the function.

Value

A list of estimates containing the following components:

error

An indicator of whether the algorithm ran and converged. Returns FALSE, if it ran correctly and FALSE otherwise.

datmat

A data frame returning datmat with the updated estimates for the nuisance functions q10, q02 and q12. This is returned only if error is FALSE.

References

van der Laan, M. J. and Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1)

Das, M., Kennedy, E. H., & Jewell, N.P. (2021). Doubly robust capture-recapture methods for estimating population size. arXiv preprint arXiv:2104.14091.

Examples

data = matrix(sample(c(0,1), 2000, replace = TRUE), ncol = 2)
xmat = matrix(runif(nrow(data)*3, 0, 1), nrow = nrow(data))
datmat = cbind(data, data[,1]*data[,2], xmat)
colnames(datmat) = c("yj", "yk", "yjk", "q10", "q02", "q12")
datmat = as.data.frame(datmat)
result = tmle(datmat, margin = 0.005, stop_margin = 0.00001, twolist = TRUE)