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
The most useful methods are for classes
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
summary(object, ...) S4 (NMF) `summary`(object, class, target)
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.
signature(object = "NMF"): Computes summary measures for a single NMF model.
The following measures are computed:
signature(object = "NMFfit"): Computes summary measures for a single fit from
This method adds the following measures to the measures
computed by the method
NMFfitobjects, this element is always equal to the value in cpu, but will be different for multiple-run fits.
NMFfitobjects, but will vary for multiple-run fits.
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
NMF-classfor details on the computed measures. In addition, the cophenetic correlation (
dispersioncoefficients of the consensus matrix are returned, as well as the total CPU time (