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Fit an SVM novelty detection model to estimate an applicability domain.

apd_svm_novel_detection.Rd

apd_svm_novel_detection() fits an 'one-svc' novelty detection model.

Usage

apd_svm_novel_detection(x, ...)

# Default S3 method
apd_svm_novel_detection(x, ...)

# S3 method for class 'data.frame'
apd_svm_novel_detection(x, ...)

# S3 method for class 'matrix'
apd_svm_novel_detection(x, ...)

# S3 method for class 'formula'
apd_svm_novel_detection(formula, data, ...)

# S3 method for class 'recipe'
apd_svm_novel_detection(x, data, ...)

Arguments

x

Depending on the context:

  • A data frame of predictors.

  • A matrix of predictors.

  • A recipe specifying a set of preprocessing steps created from recipes::recipe().

...

Options to pass to kernlab::ksvm(). Options should not include data.

formula

A formula specifying the predictor terms on the right-hand side. No outcome should be specified.

data

When a recipe or formula is used, data is specified as:

  • A data frame containing the predictors.

Value

A apd_svm_novel_detection object.