optimize.model: Model Optimization and Metrics
In cdeterman/OmicsMarkeR: Classification and Feature Selection for 'Omics' Datasets

Description Usage Arguments Value Author(s)

Optimizes each model based upon the parameters provided either by the internal denovo.grid function or by the user.

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optimize.model(trainVars, trainGroup, method, k.folds = 10, repeats = 3,
  res = 3, grid = NULL, metric = "Accuracy", allowParallel = FALSE,
  verbose = "none", theDots = NULL)

`trainVars`	Data used to fit the model
`trainGroup`	Group identifiers for the training data
`method`	A vector of strings listing models to be optimized
`k.folds`	Number of folds generated during cross-validation. Default `"k.folds = 10"`
`repeats`	Number of times cross-validation repeated. Default `"repeats = 3"`
`res`	Resolution of model optimization grid. Default `"res = 3"`
`grid`	Optional list of grids containing parameters to optimize for each algorithm. Default `"grid = NULL"` lets function create grid determined by `"res"`
`metric`	Criteria for model optimization. Available options are `"Accuracy"` (Predication Accuracy), `"Kappa"` (Kappa Statistic), and `"AUC-ROC"` (Area Under the Curve - Receiver Operator Curve)
`allowParallel`	Logical argument dictating if parallel processing is allowed via foreach package
`verbose`	Character argument specifying how much output progress to print. Options are 'none', 'minimal' or 'full'.
`theDots`	List of additional arguments provided in the initial classification and features selection function

Basically a list with the following elements:

`method`	Vector of strings listing models that were optimized
`performance`	Performance generated internally to optimize model
`bestTune`	List of paramaters chosen for each model
`dots`	List of extra arguments initially provided
`metric`	Criteria that was used for model optimization
`finalModels`	The fitted models with the 'optimum' parameters
`performance.metrics`	The performance metrics calculated internally for each resulting prediction
`tune.metrics`	The results from each tune
`perfNames`	The names of the performance metrics
`comp.catch`	If the optimal PLSDA model contains only 1 component, the model must be refit with 2 components. This catches the 1 component parameter so feature selection and further performance analysis can be conducted on the 1 component.