Structure for Storing the Best Fit Amongst Multiple NMF Runs
This class is used to return the result from a multiple
run of a single NMF algorithm performed with function
nmf with the – default – option
keep.all=FALSE (cf. nmf).
Beside the best fit, this class allows to hold data about the computation of the multiple runs, such as the number of runs, the CPU time used to perform all the runs, as well as the consensus matrix.
Due to the inheritance from class NMFfit, objects
of class NMFfitX1 can be handled exactly as the
results of single NMF run – as if only the best run had
been performed.
object of class matrix used to
store the consensus matrix based on all the runs.
an integer that contains the number of
runs performed to compute the object.
an object that contains RNG settings used for
the first run. See getRNG1.
signature(object = "NMFfitX1"):
The result is the matrix stored in slot
‘consensus’. This method returns NULL if
the consensus matrix is empty.
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 = "NMFfitX1"):
Returns the RNG settings used to compute the first of all
NMF runs, amongst which object was selected as the
best fit.
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(x = "NMFfitX1", y =
"NMFfitX1"): Compares the NMF models fitted by multiple
runs, that only kept the best fits.
signature(object = "NMFfitX1"):
Returns the number of NMF runs performed, amongst which
object was selected as the best fit.
signature(object = "NMFfitX1"): Show
method for objects of class NMFfitX1
Other multipleNMF: NMFfitX-class,
NMFfitXn-class
# generate a synthetic dataset with known classes n <- 15; counts <- c(5, 2, 3); V <- syntheticNMF(n, counts) # get the class factor groups <- V$pData$Group # perform multiple runs of one algorithm, keeping only the best fit (default) #i.e.: the implicit nmf options are .options=list(keep.all=FALSE) or .options='-k' res <- nmf(V, 3, nrun=2) res # compute summary measures summary(res) # get more info summary(res, target=V, class=groups) # show computational time runtime.all(res) # plot the consensus matrix, as stored (pre-computed) in the object ## Not run: consensusmap(res, annCol=groups)
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