cloutput-class {CMA} | R Documentation

## "cloutput"

### Description

Object returned by one of the classifiers (functions ending with `CMA`)

### Slots

`learnind`:

  Vector of indices that indicates which observations where used in the learning set.

`y`:

  Actual (true) class labels of predicted observations.

`yhat`:

  Predicted class labels by the classifier.

`prob`:

  A `numeric` `matrix` whose rows equals the number of predicted observations (length of `y`/`yhat`) and whose columns equal the number of different classes in the learning set. Rows add up to one. Entry `j,k` of this matrix contains the probability for the `j`-th predicted observation to belong to class `k`. Can be a matrix of `NA`s, if the classifier used does not provide any probabilities

`method`:

  Name of the classifer used.

`mode`:

  `character`, one of `"binary"` (if the number of classes in the learning set is two) or `multiclass` (if it is more than two).

`model`:

  List containing the constructed classifiers.

### Methods

show

  Use `show(cloutput-object)` for brief information

ftable

  Use `ftable(cloutput-object)` to obtain a confusion matrix/cross-tabulation of `y` vs. `yhat`, s. `ftable,cloutput-method`.

plot

  Use `plot(cloutput-object)` to generate a probability plot of the matrix `prob` described above, s. `plot,cloutput-method`

roc

  Use `roc(cloutput-object)` to compute the empirical ROC curve and the Area Under the Curve (AUC) based on the predicted probabilities, s.`roc,cloutput-method`

### Author(s)

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

### See Also

`clvarseloutput` `compBoostCMA`, `dldaCMA`, `ElasticNetCMA`, `fdaCMA`, `flexdaCMA`, `gbmCMA`, `knnCMA`, `ldaCMA`, `LassoCMA`, `nnetCMA`, `pknnCMA`, `plrCMA`, `pls_ldaCMA`, `pls_lrCMA`, `pls_rfCMA`, `pnnCMA`, `qdaCMA`, `rfCMA`, `scdaCMA`, `shrinkldaCMA`, `svmCMA`

[Package CMA version 1.50.0 Index]