Testing NMF Objects
The functions documented here tests different characteristics of NMF objects.
is.nmf tests if an object is an NMF model or a
class that extends the class NMF.
hasBasis tests whether an objects contains a basis
matrix – returned by a suitable method basis –
with at least one row.
hasBasis tests whether an objects contains a
coefficient matrix – returned by a suitable method
coef – with at least one column.
is.partial.nmf tests whether an NMF model object
contains either an empty basis or coefficient matrix. It
is a shorcut for !hasCoef(x) || !hasBasis(x).
is.nmf(x) is.empty.nmf(x, ...) hasBasis(x) hasCoef(x) is.partial.nmf(x) isNMFfit(object, recursive = TRUE)
x |
an R object. See section Details, for how each function uses this argument. |
... |
extra parameters to allow extension or passed to subsequent calls |
object |
any R object. |
recursive |
if |
is.nmf tests if object is the name of a
class (if a character string), or inherits from a
class, that extends NMF.
is.empty.nmf returns TRUE if the basis and
coefficient matrices of x have respectively zero
rows and zero columns. It returns FALSE otherwise.
In particular, this means that an empty model can still
have a non-zero number of basis components, i.e. a
factorization rank that is not null. This happens, for
example, in the case of NMF models created calling the
factory method nmfModel with a value only
for the factorization rank.
isNMFfit checks if object inherits from
class NMFfit or
NMFfitX, which are the two types of
objects returned by the function nmf. If
object is a plain list and
recursive=TRUE, then the test is performed on each
element of the list, and the return value is a logical
vector (or a list if object is a list of list) of
the same length as object.
isNMFfit returns a logical vector (or a
list if object is a list of list) of the same
length as object.
The function is.nmf does some extra work with the
namespace as this function needs to return correct
results even when called in .onLoad. See
discussion on r-devel:
https://stat.ethz.ch/pipermail/r-devel/2011-June/061357.html
#---------- # is.nmf #---------- # test if an object is an NMF model, i.e. that it implements the NMF interface is.nmf(1:4) is.nmf( nmfModel(3) ) is.nmf( nmf(rmatrix(10, 5), 2) ) #---------- # is.empty.nmf #---------- # empty model is.empty.nmf( nmfModel(3) ) # non empty models is.empty.nmf( nmfModel(3, 10, 0) ) is.empty.nmf( rnmf(3, 10, 5) ) #---------- # isNMFfit #---------- ## Testing results of fits # generate a random V <- rmatrix(20, 10) # single run -- using very low value for maxIter to speed up the example res <- nmf(V, 3, maxIter=3L) isNMFfit(res) # multiple runs - keeping single fit resm <- nmf(V, 3, nrun=2, maxIter=3L) isNMFfit(resm) # with a list of results isNMFfit(list(res, resm, 'not a result')) isNMFfit(list(res, resm, 'not a result'), recursive=FALSE)
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