autoMrP (version 1.0.6)

ebma_mc_tol: EBMA multicore tuning - parallelises over tolerance values.

Description

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

Usage

ebma_mc_tol(
  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

The data used for EBMA tuning. A tibble.

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

The tolerance values used for EBMA. A numeric vector.

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.

Examples

if (FALSE) {
# not yet
}