iterateModel: Impute missing estimates, with Smart Guessing

In saharaja/imputeORS: Impute Missing Values in the BLS Occupational Requirements Survey

Description

Function to impute missing estimates in an iterative fashion. The first prediction (Iteration 0) is the result of the smart guessing procedure. All subsequent iterations rely on XGBoost to produce preliminary predictions. These predictions are then adjusted to adhere to boundary conditions on the data (all estimates must be in the range [0,1], and the sum of all estimates within an occupational group must be <=1).

Usage

iterateModel(
  ors.data.sims,
  n.iter,
  weight.step,
  mdl.d = 14,
  mdl.n = 200,
  mdl.e = 0.6,
  sg.soc.code
)

Arguments

ors.data.sims	Original data augmented with relevant predictors, i.e. all records, including both known and missing estimates, as well as simulated data (output of computeSimulations())
n.iter	Number of times to iterate/adjust the model
weight.step	Increment by which to increase modeling weight of missing data with each iteration
mdl.d	Tree model maximum depth; default is 14
mdl.n	Tree model rounds; default is 200
mdl.e	Tree model eta; default is 0.6
sg.soc.code	SOC code to use for smart guessing, either "upSOC2", "upSOC3", or "upSOC4"

Value

A list containing the results of iterative modeling, for each simulation

See Also

computeSimulations()

smartGuess()

xgboost::xgboost()

doParallel::registerDoParallel()

parallel::makeCluster()

saharaja/imputeORS documentation built on Feb. 4, 2022, 12:27 a.m.