tmlenet_options: Setting Options for 'tmlenet'
In osofr/tmlenet: Targeted Maximum Likelihood Estimation for Network Data

Description Usage Arguments Value See Also

Additional options that control the estimation algorithm in tmlenet package

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tmlenet_options(bin_estimator = speedglmR6$new(), parfit = FALSE,
  bin.method = c("equal.len", "equal.mass", "dhist"), nbins = NA,
  maxncats = 20, poolContinVar = FALSE, maxNperBin = 1000)

`bin_estimator`	The estimator to use for fitting the binary outcomes (defaults to `speedglmR6` which estimates with `speedglmR6`) another default option is `glmR6`.
`parfit`	Default is `FALSE`. Set to `TRUE` to use `foreach` package and its functions `foreach` and `dopar` to perform parallel logistic regression fits and predictions for discretized continuous outcomes. This functionality requires registering a parallel backend prior to running `tmlenet` function, e.g., using `doParallel` R package and running `registerDoParallel(cores = ncores)` for integer `ncores` parallel jobs. For an example, see a test in "./tests/RUnit/RUnit_tests_04_netcont_sA_tests.R".
`bin.method`	The method for choosing bins when discretizing and fitting the conditional continuous summary exposure variable `sA`. The default method is `"equal.len"`, which partitions the range of `sA` into equal length `nbins` intervals. Method `"equal.mass"` results in a data-adaptive selection of the bins based on equal mass (equal number of observations), i.e., each bin is defined so that it contains an approximately the same number of observations across all bins. The maximum number of observations in each bin is controlled by parameter `maxNperBin`. Method `"dhist"` uses a mix of the above two approaches, see Denby and Mallows "Variations on the Histogram" (2009) for more detail.
`nbins`	Set the default number of bins when discretizing a continous outcome variable under setting `bin.method = "equal.len"`. If left as `NA` the total number of equal intervals (bins) is determined by the nearest integer of `nobs`/`maxNperBin`, where `nobs` is the total number of observations in the input data.
`maxncats`	Max number of unique categories a categorical variable `sA[j]` can have. If `sA[j]` has more it is automatically considered continuous.
`poolContinVar`	Set to `TRUE` for fitting a pooled regression which pools bin indicators across all bins. When fitting a model for binirized continuous outcome, set to `TRUE` for pooling bin indicators across several bins into one outcome regression?
`maxNperBin`	Max number of observations per 1 bin for a continuous outcome (applies directly when `bin.method="equal.mass"` and indirectly when `bin.method="equal.len"`, but `nbins = NA`).