run_gb_mc: GB multicore tuning.

Description

run_gb_mc is called from within run_gb. It tunes using multiple cores.

Usage

run_gb_mc(
  y,
  L1.x,
  L2.eval.unit,
  L2.unit,
  L2.reg,
  form,
  gb.grid,
  n.minobsinnode,
  loss.unit,
  loss.fun,
  data,
  cores
)

Value

The tuning parameter combinations and there associated loss function scores. A list.

Arguments

y: Outcome variable. A character vector containing the column names of the outcome variable. A character scalar containing the column name of the outcome variable in survey.
L1.x: Individual-level covariates. A character vector containing the column names of the individual-level variables in survey and census used to predict outcome y. Note that geographic unit is specified in argument L2.unit.
L2.eval.unit: Geographic unit for the loss function. A character scalar containing the column name of the geographic unit in survey and census.
L2.unit: Geographic unit. A character scalar containing the column name of the geographic unit in survey and census at which outcomes should be aggregated.
L2.reg: Geographic region. A character scalar containing the column name of the geographic region in survey and census by which geographic units are grouped (L2.unit must be nested within L2.reg). Default is NULL.
form: The model formula. A formula object.
gb.grid: The hyper-parameter search grid. A matrix of all hyper-parameter combinations.
n.minobsinnode: GB minimum number of observations in the terminal nodes. An integer-valued scalar specifying the minimum number of observations that each terminal node of the trees must contain. Default is \(5\).
loss.unit: Loss function unit. A character-valued scalar indicating whether performance loss should be evaluated at the level of individual respondents (individuals) or geographic units (L2 units). Default is individuals.
loss.fun: Loss function. A character-valued scalar indicating whether prediction loss should be measured by the mean squared error (MSE) or the mean absolute error (MAE). Default is MSE.
data: Data for cross-validation. A list of \(k\) data.frames, one for each fold to be used in \(k\)-fold cross-validation.
cores: The number of cores to be used. An integer indicating the number of processor cores used for parallel computing. Default is 1.