plCV: Perform Simple Cross-Validation
In tpq/exprso: Rapid Deployment of Machine Learning Algorithms

Description Usage Arguments Details Value

Calculates v-fold or leave-one-out cross-validation without selecting a new set of features with each fold. See Details.

1	plCV(array, top, how, fold, aucSkip, plCV.acc, ...)

`array`	Specifies the `ExprsArray` object to undergo cross-validation.
`top`	A numeric scalar or character vector. A numeric scalar indicates the number of top features that should undergo feature selection. A character vector indicates specifically which features by name should undergo feature selection. Set `top = 0` to include all features. Note that providing a numeric vector for the `top` argument will have `plGrid` search across multiple top features. However, by providing a list of numeric vectors as the `top` argument, the user can force the default handling of numeric vectors.
`how`	A character string. The `build` method to iterate.
`fold`	A numeric scalar. The number of folds for cross-validation. Set `fold = 0` to perform leave-one-out cross-validation. Argument passed to `plCV`. Set `fold = NULL` to skip cross-validation altogether.
`aucSkip`	A logical scalar. Argument passed to `calcStats`.
`plCV.acc`	A string. The performance metric to use. For example, choose from "acc", "sens", "spec", "prec", "f1", "auc", or any of the regression specific measures. Argument passed to `plCV`.
`...`	Arguments passed to the `how` method. Unlike the `build` method, `plGrid` allows multiple parameters for each argument, supplied as a vector. See Details.

plCV performs v-fold or leave-one-out cross-validation. The argument fold specifies the number of v-folds to use during cross-validation. Set fold = 0 to perform leave-one-out cross-validation.

This type of cross-validation is most appropriate if the data has not undergone any prior feature selection. However, it is also useful as an unbiased guide to parameter selection within another pl workflow.

Users should never need to call this function directly. Instead, they should use plMonteCarlo or plNested. There, plCV handles inner-fold cross-validation.

The average inner-fold cross-validation accuracy.

tpq/exprso documentation built on July 27, 2019, 8:44 a.m.