loo {cancerclass} | R Documentation

## Leave-one-out cross-validation

### Description

Fitting and validation of a predictor in a leave-one-out protocol.

### Usage

      loo(eset, class="class", method = "welch.test", ngenes=50, dist="cor", hparam = 0.75, positive="")

### Arguments

`eset` | Bioconductor ExpressionSet
`class` | String specifying the column in pData(eset) that contains the class information.
`method` | Specifying the feature selection method. Possible values are "cor", "student.test", "welch.test", "wilcoxon.test", "foldchange", "copa", "os", "ort", "shift", "throw".
`ngenes` | Number of features used for classification.
`dist` | Metric for distance calculation
`hparam` | Hyperparameter needed for some of the feature selection methods. For methods copa, ors and os: Quantile (e.g. 0.75, 0.9, 0.95) used in order to detect outliers. For methods shift and throw: the minimum number of samples in each class after applying shift or throw.
`positive` | One of the two classes. Membership to this class is considered as positive. Needed in order to calculate sensitivity and specificity of the validation.

### Details

A leave-one-out cross-validation is performend by calling fit and predict in a loop.

### Value

A `pvalidation` object, see `pvalidation.object` for details.

### Examples

    ### see: help(GOLUB);

[Package cancerclass version 1.30.0 Index]