ebma_mc_draws: EBMA multicore tuning - parallelises over draws.

In autoMrP: Improving MrP with Ensemble Learning

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
)

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.

Value

The classifier weights. A numeric vector.

autoMrP documentation built on May 29, 2024, 6:40 a.m.