run_gb_mc {autoMrP}

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
)

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.

Value

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