The functions fit and minfit are S4
genetics that extract the best model object and the best
fit object respectively, from a collection of models or
from a wrapper object.
fit<- sets the fitted model in a fit object. It is
meant to be called only when developing new NMF
algorithms, e.g. to update the value of the model stored
in the starting point.
fit(object, ...) fit(object)<-value
minfit(object, ...)
an object fitted by some algorithm, e.g. as
returned by the function nmf.
replacement value
extra arguments to allow extension
signature(object = "NMFfit"): Returns
the NMF model object stored in slot 'fit'.
signature(object = "NMFfitX"): Returns
the model object that achieves the lowest residual
approximation error across all the runs.
It is a pure virtual method defined to ensure fit
is defined for sub-classes of NMFfitX, which
throws an error if called.
signature(object = "NMFfitX1"): Returns
the model object associated with the best fit, amongst
all the runs performed when fitting object.
Since NMFfitX1 objects only hold the best fit,
this method simply returns the NMF model fitted by
object -- that is stored in slot ‘fit’.
signature(object = "NMFfitXn"): Returns
the best NMF fit object amongst all the fits stored in
object, i.e. the fit that achieves the lowest
estimation residuals.
signature(object = "NMFfit", value =
"NMF"): Updates the NMF model object stored in slot
'fit' with a new value.
signature(object = "NMFfit"):
Returns the object its self, since there it is the result
of a single NMF run.
signature(object = "NMFfitX"):
Returns the fit object that achieves the lowest residual
approximation error across all the runs.
It is a pure virtual method defined to ensure
minfit is defined for sub-classes of
NMFfitX, which throws an error if called.
signature(object = "NMFfitX1"):
Returns the fit object associated with the best fit,
amongst all the runs performed when fitting
object.
Since NMFfitX1 objects only hold the best fit,
this method simply returns object coerced into an
NMFfit object.
signature(object = "NMFfitXn"):
Returns the best NMF model in the list, i.e. the run that
achieved the lower estimation residuals.
The model is selected based on its deviance value.
A fit object differs from a model object in that it contains data about the fit, such as the initial RNG settings, the CPU time used, etc..., while a model object only contains the actual modelling data such as regression coefficients, loadings, etc...
That best model is generally defined as the one that achieves the maximum/minimum some quantitative measure, amongst all models in a collection.
In the case of NMF models, the best model is the one that achieves the best approximation error, according to the objective function associated with the algorithm that performed the fit(s).