Usage
## S3 method for class 'NMF':
connectivity(x, ...)
## S3 method for class 'NMF,factor':
entropy(x, class, ...)
## S3 method for class 'NMF':
evar(object, target)
## S3 method for class 'NMFfit':
residuals(object, track=FALSE, ...)
rss(object, ...)
## S3 method for class 'NMF':
rss(object, target)
## S3 method for class 'NMF':
featureScore(object, method=c('kim', 'max'))
## S3 method for class 'NMF':
extractFeatures(object, method=c('kim', 'max')
, format=c('list', 'combine', 'subset'))
## S3 method for class 'NMF':
metaHeatmap(object, what=c('samples', 'features'), filter=FALSE, ...)
## S3 method for class 'NMF':
nmfApply(object, MARGIN, FUN, ...)
## S3 method for class 'NMFfit':
plot(x, ...)
## S3 method for class 'NMF':
predict(object, what = c('samples', 'features'), prob=FALSE)
## S3 method for class 'NMF,factor':
purity(x, class, ...)
randomize(x, ...)
## S3 method for class 'NMF':
sparseness(x)
syntheticNMF(n, r, p, offset=NULL, noise=FALSE, return.factors=FALSE)Arguments
class
A factor giving a known class membership for each sample.
In methods entropy and purity, argument class is coerce to a
factor if necessary.
filter
Relevant when what='features'. It specifies how to filter the features that
will appear in the heatmap. When FALSE (default), all the features are used.
Other possible values are:
format
the output format of the extracted features.
Possible values are:
list(default) a list with one element per basis vector, each containing
the indices of the basis-specific features.combinea single intege
FUN
the function to be applied: see 'Details'. In the case of
functions like +, %*%, etc., the function name must be
backquoted or quoted.
See link[base]{apply} for more details.
MARGIN
a vector giving the subscripts which the function will be
applied over. 1 indicates rows, 2' indicates columns,
c(1,2) indicates rows and columns.
See link[base]{apply} for more details.
method
Method used to compute the feature scores and selecting the features.
Possible values are:
kim(default) to use Kim and Park (2007) scoring schema and feature selection
method.
The features are first scored using the fun
n
Number of rows of the synthetic target matrix.
noise
if TRUE, a random noise is added the target matrix.
object
A matrix or an object that inherits from class
NMF or NMFfit -- depending on the method.
offset
a vector giving the offset to add to the synthetic target matrix.
Its length should be equal to the number of rows n.
prob
Should the probability associated with each cluster prediction be
computed and returned.
p
Number of columns of the synthetic target matrix. Not used if parameter
r is a vector (see description of argument r).
r
Underlying factorization rank. If a single numeric is given,
the classes are randomly generated from a multinomial distribution.
If a numerical vector is given, then it should contain the counts in the different
classes (i.e integers
return.factors
If TRUE, the underlying matrices W and
H are also returned.
target
the target object estimated by model object. It can be
a matrix or an ExpressionSet.
track
if TRUE, the whole residuals track is returned.
Otherwise only the last residuals computed is returned.
what
Specifies on which matrix, basis components (what='features')
or mixture coefficients (what='samples') the computation should be performed.
x
for randomize: the matrix or ExpressionSet object whose
entries will be randomised.
for plot: An object that inherits from class NMFfit.
otherwis
...
Used to pass extra parameters to subsequent calls:
- in
metaHeatmap: Graphical parameters passed to functionheatmap.2 - in
nmfApply: optional arguments to func