loo: Leave-one-out cross-validation

In cancerclass: Development and validation of diagnostic tests from high-dimensional molecular data

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);

cancerclass documentation built on Nov. 8, 2020, 5:31 p.m.