Description Details Slots Methods See Also Examples

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`

).

It extends both classes `NMFfitX`

and
`NMFfit`

, and stores a the result of
the best fit in its `NMFfit`

structure.

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.

- consensus
object of class

`matrix`

used to store the consensus matrix based on all the runs.- nrun
an

`integer`

that contains the number of runs performed to compute the object.- rng1
an object that contains RNG settings used for the first run. See

`getRNG1`

.

- consensus
`signature(object = "NMFfitX1")`

: The result is the matrix stored in slot ‘consensus’. This method returns`NULL`

if the consensus matrix is empty.- fit
`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’.- getRNG1
`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.- minfit
`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.- nmf.equal
`signature(x = "NMFfitX1", y = "NMFfitX1")`

: Compares the NMF models fitted by multiple runs, that only kept the best fits.- nrun
`signature(object = "NMFfitX1")`

: Returns the number of NMF runs performed, amongst which`object`

was selected as the best fit.- show
`signature(object = "NMFfitX1")`

: Show method for objects of class`NMFfitX1`

Other multipleNMF: `NMFfitX-class`

,
`NMFfitXn-class`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
# generate a synthetic dataset with known classes
n <- 20; 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=3)
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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