Structure for Storing All Fits from Multiple NMF Runs

Description

This class is used to return the result from a multiple run of a single NMF algorithm performed with function nmf with option keep.all=TRUE (cf. nmf).

Details

It extends both classes NMFfitX-class and list, and stores the result of each run (i.e. a NMFfit object) in its list structure.

IMPORTANT NOTE: This class is designed to be read-only, even though all the list-methods can be used on its instances. Adding or removing elements would most probably lead to incorrect results in subsequent calls. Capability for concatenating and merging NMF results is for the moment only used internally, and should be included and supported in the next release of the package.

Slots

  1. .Datastandard slot that contains the S3 list object data. See R documentation on S3/S4 classes for more details (e.g., setOldClass).

Methods

  1. algorithmsignature(object = "NMFfitXn"): Returns the name of the common NMF algorithm used to compute all fits stored in object

    Since all fits are computed with the same algorithm, this method returns the name of algorithm that computed the first fit. It returns NULL if the object is empty.

  2. basissignature(object = "NMFfitXn"): Returns the basis matrix of the best fit amongst all the fits stored in object. It is a shortcut for basis(fit(object)).

  3. coefsignature(object = "NMFfitXn"): Returns the coefficient matrix of the best fit amongst all the fits stored in object. It is a shortcut for coef(fit(object)).

  4. comparesignature(object = "NMFfitXn"): Compares the fits obtained by separate runs of NMF, in a single call to nmf.

  5. consensussignature(object = "NMFfitXn"): This method returns NULL on an empty object. The result is a matrix with several attributes attached, that are used by plotting functions such as consensusmap to annotate the plots.

  6. dimsignature(x = "NMFfitXn"): Returns the dimension common to all fits.

    Since all fits have the same dimensions, it returns the dimension of the first fit. This method returns NULL if the object is empty.

  7. entropysignature(x = "NMFfitXn", y = "ANY"): Computes the best or mean entropy across all NMF fits stored in x.

  8. fitsignature(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.

  9. .getRNGsignature(object = "NMFfitXn"): Returns the RNG settings used for the best fit.

    This method throws an error if the object is empty.

  10. getRNG1signature(object = "NMFfitXn"): Returns the RNG settings used for the first run.

    This method throws an error if the object is empty.

  11. minfitsignature(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.

  12. modelnamesignature(object = "NMFfitXn"): Returns the common type NMF model of all fits stored in object

    Since all fits are from the same NMF model, this method returns the model type of the first fit. It returns NULL if the object is empty.

  13. nbasissignature(x = "NMFfitXn"): Returns the number of basis components common to all fits.

    Since all fits have been computed using the same rank, it returns the factorization rank of the first fit. This method returns NULL if the object is empty.

  14. nrunsignature(object = "NMFfitXn"): Returns the number of runs performed to compute the fits stored in the list (i.e. the length of the list itself).

  15. puritysignature(x = "NMFfitXn", y = "ANY"): Computes the best or mean purity across all NMF fits stored in x.

  16. runtime.allsignature(object = "NMFfitXn"): If no time data is available from in slot ‘runtime.all’ and argument null=TRUE, then the sequential time as computed by seqtime is returned, and a warning is thrown unless warning=FALSE.

  17. seedingsignature(object = "NMFfitXn"): Returns the name of the common seeding method used the computation of all fits stored in object

    Since all fits are seeded using the same method, this method returns the name of the seeding method used for the first fit. It returns NULL if the object is empty.

  18. seqtimesignature(object = "NMFfitXn"): Returns the CPU time that would be required to sequentially compute all NMF fits stored in object.

    This method calls the function runtime on each fit and sum up the results. It returns NULL on an empty object.

  19. showsignature(object = "NMFfitXn"): Show method for objects of class NMFfitXn

Examples



# 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 all the fits
res <- nmf(V, 3, nrun=3, .options='k') # .options=list(keep.all=TRUE) also works

## # NOTE - CRAN check detected: limiting maximum number of cores [2/4]
## # NOTE - CRAN check detected: limiting maximum number of cores [2/4]

res

## <Object of class: NMFfitXn >
##   Method: brunet 
##   Runs:  3 
##   RNG:
##    407L, 1934160049L, -162350402L, 779694119L, -1901819332L, 520338957L, -994911894L 
##   Total timing:
##    user  system elapsed 
##   2.500   0.233   1.801 
##   Sequential timing:
##    user  system elapsed 
##   0.364   0.000   0.366

summary(res)

##      Length Class  Mode
## [1,] 1      NMFfit S4  
## [2,] 1      NMFfit S4  
## [3,] 1      NMFfit S4

# get more info
summary(res, target=V, class=groups)

##      Length Class  Mode
## [1,] 1      NMFfit S4  
## [2,] 1      NMFfit S4  
## [3,] 1      NMFfit S4

# compute/show computational times
runtime.all(res)

##    user  system elapsed 
##   2.500   0.233   1.801

seqtime(res)

##    user  system elapsed 
##   0.364   0.000   0.366

# plot the consensus matrix, computed on the fly
## Not run:  consensusmap(res, annCol=groups)

See also

Other multipleNMF: NMFfitX1-class, NMFfitX-class