ebma_mc_tol  R Documentation 
ebma_mc_tol
is called from within ebma
. It tunes using
multiple cores.
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
)
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 
L1.x 
Individuallevel covariates. A character vector containing the
column names of the individuallevel variables in 
L2.x 
Contextlevel covariates. A character vector containing the
column names of the contextlevel variables in 
L2.unit 
Geographic unit. A character scalar containing the column
name of the geographic unit in 
L2.reg 
Geographic region. A character scalar containing the column
name of the geographic region in 
pc.names 
Principal Component Variable names. A character vector containing the names of the contextlevel principal components variables. 
model.bs 
The tuned model from the multilevel regression with best
subset selection classifier. An 
model.pca 
The tuned model from the multilevel regression with
principal components as contextlevel predictors classifier. An

model.lasso 
The tuned model from the multilevel regression with L1
regularization classifier. A 
model.gb 
The tuned model from the gradient boosting classifier. A

model.svm 
The tuned model from the support vector machine classifier.
An 
model.mrp 
The standard MrP model. An 
model_deep 
The tuned model from the deep mrp classifier. An

tol 
The tolerance values used for EBMA. A numeric vector. 
n.draws 
EBMA number of samples. An integervalued scalar specifying
the number of bootstrapped samples to be drawn from the EBMA fold and used
for tuning EBMA. Default is 
cores 
The number of cores to be used. An integer indicating the number of processor cores used for parallel computing. Default is 1. 
The classifier weights. A numeric vector.
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