ebma_mc_draws: EBMA multicore tuning - parallelises over draws.

Description

ebma_mc_draws is called from within ebma. It tunes using multiple cores.

Usage

ebma_mc_draws(
  train.preds,
  train.y,
  ebma.fold,
  y,
  L1.x,
  L2.x,
  L2.unit,
  L2.reg,
  pc.names,
  model.bs,
  model.pca,
  model.lasso,
  model.gb,
  model.svm,
  model.mrp,
  model_deep,
  tol,
  n.draws,
  cores
)

Value

The classifier weights. A numeric vector.

Arguments

train.preds: Predictions of classifiers on the classifier training data. A tibble.
train.y: Outcome variable of the classifier training data. A numeric vector.
ebma.fold: New data for EBMA tuning. A list containing the the data that must not have been used in classifier training.
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.x: Context-level covariates. A character vector containing the column names of the context-level variables in survey and census used to predict outcome y. To exclude context-level variables, set L2.x = NULL.
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.
pc.names: Principal Component Variable names. A character vector containing the names of the context-level principal components variables.
model.bs: The tuned model from the multilevel regression with best subset selection classifier. An glmer object.
model.pca: The tuned model from the multilevel regression with principal components as context-level predictors classifier. An glmer object.
model.lasso: The tuned model from the multilevel regression with L1 regularization classifier. A glmmLasso object.
model.gb: The tuned model from the gradient boosting classifier. A gbm object.
model.svm: The tuned model from the support vector machine classifier. An svm object.
model.mrp: The standard MrP model. An glmer object
model_deep: The tuned model from the deep mrp classifier. An vglmer object.
tol: EBMA tolerance. A numeric vector containing the tolerance values for improvements in the log-likelihood before the EM algorithm stops optimization. Values should range at least from \(0.01\) to \(0.001\). Default is c(0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005, 0.00001). Passed on from ebma.tol.
n.draws: EBMA number of samples. An integer-valued scalar specifying the number of bootstrapped samples to be drawn from the EBMA fold and used for tuning EBMA. Default is \(100\). Passed on from ebma.n.draws.
cores: The number of cores to be used. An integer indicating the number of processor cores used for parallel computing. Default is 1.