The NMF package defines methods for the generic
  deviance from the package stats, to compute
  approximation errors between NMF models and matrices,
  using a variety of objective functions.
nmfDistance returns a function that computes the
  distance between an NMF model and a compatible matrix.
deviance(object, ...) S4 (NMF) `deviance`(object, y, method = c("", "KL", "euclidean"), ...) nmfDistance(method = c("", "KL", "euclidean")) S4 (NMFfit) `deviance`(object, y, method, ...) S4 (NMFStrategy) `deviance`(object, x, y, ...)
object, i.e. y must have the same dimension
  as fitted(object).(x="NMF", y="matrix", ...) that
  implements a distance measure between an NMF model
  x and a target matrix y, i.e. an objective
  function to use to compute the deviance. In
  deviance, it is passed to nmfDistance to
  get the function that effectively computes the deviance.y.deviance returns a nonnegative numerical value
nmfDistance returns a function with least two
  arguments: an NMF model and a matrix.
signature(object = "NMF"):
  Computes the distance between a matrix and the estimate
  of an NMF model.
  
      signature(object = "NMFfit"):
  Returns the deviance of a fitted NMF model.
  
      This method returns the final residual value if the
  target matrix y is not supplied, or the
  approximation error between the fitted NMF model stored
  in object and y. In this case, the
  computation is performed using the objective function
  method if not missing, or the objective of the
  algorithm that fitted the model (stored in slot
  'distance').
If not computed by the NMF algorithm itself, the value is
  automatically computed at the end of the fitting process
  by the function nmf, using the objective
  function associated with the NMF algorithm, so that it
  should always be available.
signature(object = "NMFfitX"):
  Returns the deviance achieved by the best fit object,
  i.e. the lowest deviance achieved across all NMF runs.
  
      signature(object = "NMFStrategy"):
  Computes the value of the objective function between the
  estimate x and the target y.