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 |
Individual-level covariates. A character vector containing the
column names of the individual-level variables in |
L2.x |
Context-level covariates. A character vector containing the
column names of the context-level 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 context-level 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 context-level 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 integer-valued 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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