The NMF package defines `summary`

methods for
different classes of objects, which helps assessing and
comparing the quality of NMF models by computing a set of
quantitative measures, e.g. with respect to their ability
to recover known classes and/or the original target
matrix.

The most useful methods are for classes
`NMF-class`

, `NMFfit-class`

,
`NMFfitX-class`

and
`NMFList-class`

, which compute summary
measures for, respectively, a single NMF model, a single
fit, a multiple-run fit and a list of heterogenous fits
performed with the function `nmf`

.

```
summary(object, ...)
S4 (NMF)
`summary`(object, class, target)
```

- object
- an NMF object. See available methods in
section
*Methods*. - ...
- extra arguments passed to the next
`summary`

method. - class
- known classes/cluster of samples specified
in one of the formats that is supported by the functions
`entropy`

and`purity`

. - target
- target matrix specified in one of the
formats supported by the functions
`rss`

and`evar`

Due to the somehow hierarchical structure of the classes
mentionned in *Description*, their respective
`summary`

methods call each other in chain, each
super-class adding some extra measures, only relevant for
objects of a specific class.

- summary
`signature(object = "NMF")`

: Computes summary measures for a single NMF model.The following measures are computed:

- sparsenessSparseness of the
factorization computed by the function
`sparseness`

. - entropyPurity of the
clustering, with respect to known classes, computed by
the function
`purity`

. - entropyEntropy of the clustering, with respect to
known classes, computed by the function
`entropy`

. - RSSResidual Sum of
Squares computed by the function
`rss`

. - evarExplained variance computed by the function
`evar`

.

- sparsenessSparseness of the
factorization computed by the function
- summary
`signature(object = "NMFfit")`

: Computes summary measures for a single fit from`nmf`

.This method adds the following measures to the measures computed by the method

`summary,NMF`

:- residualsResidual error as measured by the objective function associated to the algorithm used to fit the model.
- niterNumber of iterations performed to achieve convergence of the algorithm.
- cpuTotal CPU time required for the fit.
- cpu.allTotal CPU time required for the fit. For
`NMFfit`

objects, this element is always equal to the value in “cpu”, but will be different for multiple-run fits. - nrunNumber of runs performed
to fit the model. This is always equal to 1 for
`NMFfit`

objects, but will vary for multiple-run fits.

- summary
`signature(object = "NMFfitX")`

: Computes a set of measures to help evaluate the quality of the*best fit*of the set. The result is similar to the result from the`summary`

method of`NMFfit`

objects. See`NMF-class`

for details on the computed measures. In addition, the cophenetic correlation (`cophcor`

) and`dispersion`

coefficients of the consensus matrix are returned, as well as the total CPU time (`runtime.all`

).