clvarseloutput-class {CMA} | R Documentation

## "clvarseloutput"

### Description

Object returned by all classifiers that can peform variable selection or compute variable importance. These are:

- Random Forest, s. `rfCMA`,
- Componentwise Boosting, s. `compBoostCMA`,
- LASSO-logistic regression, s. `LassoCMA`,
- ElasticNet-logistic regression, s. `ElasticNetCMA`

. Objects of class `clvarseloutput` extend both the class `cloutuput` and `varsel`, s. below.

### 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).
`varsel`:
  `numeric` vector of variable importance measures (for Random Forest) or absolute values of regression coefficients (for the other three methods mentionned above) (from which the majority will be zero).

### Extends

Class `"cloutput"`, directly. Class `"varseloutput"`, directly.

### 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 martin.slawski@campus.lmu.de

Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

### See Also

`rfCMA`, `compBoostCMA`, `LassoCMA`, `ElasticNetCMA`

[Package CMA version 1.2.0 Index]