algorithmic-NMF {NMF} | R Documentation

## Generic Interface for Algorithms

### Description

The functions documented here are S4 generics that define an general interface for – optimisation – algorithms.

This interface builds upon the broad definition of an algorithm as a workhorse function to which is associated auxiliary objects such as an underlying model or an objective function that measures the adequation of the model with observed data. It aims at complementing the interface provided by the `stats` package.

### Usage

      algorithm(object, ...)

      algorithm(object, ...)<-value

      seeding(object, ...)

      seeding(object, ...)<-value

      niter(object, ...)

      niter(object, ...)<-value

      nrun(object, ...)

      objective(object, ...)

      objective(object, ...)<-value

      runtime(object, ...)

      runtime.all(object, ...)

      seqtime(object, ...)

      modelname(object, ...)

      run(object, y, x, ...)

      logs(object, ...)

      compare(object, ...)

### Arguments

`object` | an object computed using some algorithm, or that describes an algorithm itself.
`value` | replacement value
`...` | extra arguments to allow extension
`y` | data object, e.g. a target matrix
`x` | a model object used as a starting point by the algorithm, e.g. a non-empty NMF model.

### Details

`algorithm` and `algorithm<-` get/set an object that describes the algorithm used to compute another object, or with which it is associated. It may be a simple character string that gives the algorithm's names, or an object that includes the algorithm's definition itself (e.g. an `NMFStrategy` object).

`seeding` get/set the seeding method used to initialise the computation of an object, i.e. usually the function that sets the starting point of an algorithm.

`niter` and `niter<-` get/set the number of iterations performed to compute an object. The function `niter<-` would usually be called just before returning the result of an algorithm, when putting together data about the fit.

`nrun` returns the number of times the algorithm has been run to compute an object. Usually this will be 1, but may be be more if the algorithm involves multiple starting points.

`objective` and `objective<-` get/set the objective function associated with an object. Some methods for `objective` may also compute the objective value with respect to some target/observed data.

`runtime` returns the CPU time required to compute an object. This would generally be an object of class `proc_time`.

`runtime.all` returns the CPU time required to compute a collection of objects, e.g. a sequence of independent fits.

`seqtime` returns the sequential CPU time – that would be – required to compute a collection of objects. It would differ from `runtime.all` if the computations were performed in parallel.

`modelname` returns a the type of model associated with an object.

`run` calls the workhorse function that actually implements a strategy/algorithm, and run it on some data object.

`logs` returns the log messages output during the computation of an object.

`compare` compares objects obtained from running separate algorithms.

### Methods

algorithm

  `signature(object = "NMFfit")`: Returns the name of the algorithm that fitted the NMF model `object`.

algorithm

  `signature(object = "NMFList")`: Returns the method names used to compute the NMF fits in the list. It returns `NULL` if the list is empty.

  See `algorithm,NMFList-method` for more details.

algorithm

  `signature(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.

algorithm

  `signature(object = "NMFSeed")`: Returns the workhorse function of the seeding method described by `object`.

algorithm

  `signature(object = "NMFStrategyFunction")`: Returns the single R function that implements the NMF algorithm – as stored in slot `algorithm`.

algorithm

  `signature(object = "NMFStrategyOctave")`: Returns the name of the Octave/Matlab function that implements the NMF algorithm – as stored in slot `algorithm`.

  See `algorithm,NMFStrategyOctave-method` for more details.

algorithm<-

  `signature(object = "NMFSeed", value = "function")`: Sets the workhorse function of the seeding method described by `object`.

algorithm<-

  `signature(object = "NMFStrategyFunction", value = "function")`: Sets the function that implements the NMF algorithm, stored in slot `algorithm`.

algorithm<-

  `signature(object = "NMFStrategyOctave", value = "character")`: Sets the name of the Octave/Matlab function that implements the NMF algorithm. It is stored in slot `algorithm`.

compare

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

logs

  `signature(object = "ANY")`: Default method that returns the value of attribute/slot `'logs'` or, if this latter does not exists, the value of element `'logs'` if `object` is a `list`. It returns `NULL` if no logging data was found.

modelname

  `signature(object = "ANY")`: Default method which returns the class name(s) of `object`. This should work for objects representing models on their own.

  For NMF objects, this is the type of NMF model, that corresponds to the name of the S4 sub-class of `NMF`, inherited by `object`.

modelname

  `signature(object = "NMFfit")`: Returns the type of a fitted NMF model. It is a shortcut for `modelname(fit(object)`.

modelname

  `signature(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.

modelname

  `signature(object = "NMFStrategy")`: Returns the model(s) that an NMF algorithm can fit.

niter

  `signature(object = "NMFfit")`: Returns the number of iteration performed to fit an NMF model, typically with function `nmf`.

  Currently this data is stored in slot `'extra'`, but this might change in the future.

niter<-

  `signature(object = "NMFfit", value = "numeric")`: Sets the number of iteration performed to fit an NMF model.

  This function is used internally by the function `nmf`. It is not meant to be called by the user, except when developing new NMF algorithms implemented as single function, to set the number of iterations performed by the algorithm on the seed, before returning it (see `NMFStrategyFunction`).

nrun

  `signature(object = "ANY")`: Default method that returns the value of attribute ‘nrun’.

  Such an attribute my be attached to objects to keep track of data about the parent fit object (e.g. by method `consensus`), which can be used by subsequent function calls such as plot functions (e.g. see `consensusmap`). This method returns `NULL` if no suitable data was found.

nrun

  `signature(object = "NMFfitX")`: Returns the number of NMF runs performed to create `object`.

  It is a pure virtual method defined to ensure `nrun` is defined for sub-classes of `NMFfitX`, which throws an error if called.

  Note that because the `nmf` function allows to run the NMF computation keeping only the best fit, `nrun` may return a value greater than one, while only the result of the best run is stored in the object (cf. option `'k'` in method `nmf`).

nrun

  `signature(object = "NMFfit")`: This method always returns 1, since an `NMFfit` object is obtained from a single NMF run.

nrun

  `signature(object = "NMFfitX1")`: Returns the number of NMF runs performed, amongst which `object` was selected as the best fit.

nrun

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

objective

  `signature(object = "NMFfit")`: Returns the objective function associated with the algorithm that computed the fitted NMF model `object`, or the objective value with respect to a given target matrix `y` if it is supplied.

  See `objective,NMFfit-method` for more details.

runtime

  `signature(object = "NMFfit")`: Returns the CPU time required to compute a single NMF fit.

runtime

  `signature(object = "NMFList")`: Returns the CPU time required to compute all NMF fits in the list. It returns `NULL` if the list is empty. If no timing data are available, the sequential time is returned.

  See `runtime,NMFList-method` for more details.

runtime.all

  `signature(object = "NMFfit")`: Identical to `runtime`, since their is a single fit.

runtime.all

  `signature(object = "NMFfitX")`: Returns the CPU time required to compute all the NMF runs. It returns `NULL` if no CPU data is available.

runtime.all

  `signature(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`.

  See `runtime.all,NMFfitXn-method` for more details.

seeding

  `signature(object = "NMFfit")`: Returns the name of the seeding method that generated the starting point for the NMF algorithm that fitted the NMF model `object`.

seeding

  `signature(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.

seqtime

  `signature(object = "NMFList")`: 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.

seqtime

  `signature(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.

### Interface fo NMF algorithms

This interface is implemented for NMF algorithms by the classes `NMFfit`, `NMFfitX` and `NMFStrategy`, and their respective sub-classes. The examples given in this documentation page are mainly based on this implementation.

### Examples

    #----------
    # modelname,ANY-method
    #----------
    # get the type of an NMF model
    modelname(nmfModel(3))
    modelname(nmfModel(3, model='NMFns'))
    modelname(nmfModel(3, model='NMFOffset'))

    #----------
    # modelname,NMFStrategy-method
    #----------
    # get the type of model(s) associated with an NMF algorithm
    modelname( nmfAlgorithm('brunet') )
    modelname( nmfAlgorithm('nsNMF') )
    modelname( nmfAlgorithm('offset') )

[Package NMF version 0.20.6 Index]