SupportVectorMachine: Fit a support vector machine model
In 19900321/flipMultivariates: Multivariate models

Fit a support vector machine model

SupportVectorMachine(formula, data = NULL, subset = NULL,
  weights = NULL, output = "Accuracy",
  missing = "Exclude cases with missing data", cost = 1,
  seed = 12321, statistical.assumptions, show.labels = FALSE, ...)

`formula`	A formula of the form `groups ~ x1 + x2 + ...` That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators, and any transformations, interactions, or other non-additive operators apart from `.` will be ignored.
`data`	A `data.frame` from which variables specified in formula are preferentially to be taken.
`subset`	An optional vector specifying a subset of observations to be used in the fitting process, or, the name of a variable in `data`. It may not be an expression.
`weights`	An optional vector of sampling weights, or the name of a variable in `data`. It may not be an expression.
`output`	One of `"Accuracy"`, `"Prediction-Accuracy Table"` or `"Detail"`.
`missing`	How missing data is to be treated. Options: `"Error if missing data"`, `"Exclude cases with missing data"`, or `"Imputation (replace missing values with estimates)"`.
`cost`	A positive number controlling the compromoise between exactly fitting the training data (higher cost) and the ability to generalise to unseen data (lower cost).
`seed`	The random number seed.
`statistical.assumptions`	A Statistical Assumptions object.
`show.labels`	Shows the variable labels, as opposed to the labels, in the outputs, where a variables label is an attribute (e.g., attr(foo, "label")).
`...`	Other arguments to be supplied to `svm`.