Description Usage Arguments Details Examples
The functions documented here allow to compare the fits computed in different NMF runs. The fits do not need to be from the same algorithm, nor have the same dimension.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## S4 method for signature 'NMFfit'
compare(object, ...)
## S4 method for signature 'NMFfitXn'
compare(object, ...)
## S4 method for signature 'list'
compare(object, ...)
## S4 method for signature 'NMFList'
summary(object, sort.by = NULL, select = NULL, ...)
## S4 method for signature 'NMFList,missing'
plot(x, y, skip = -1L, ...)
## S4 method for signature 'NMF.rank'
consensusmap(object, ...)
## S4 method for signature 'list'
consensusmap(object, layout, Rowv = FALSE, main = names(object), ...)
|
object |
an object of class |
... |
extra arguments passed by |
sort.by |
the sorting criteria, i.e. a partial match of a column name,
by which the result |
select |
the columns to be output in the result |
x |
an |
y |
missing |
skip |
an integer that indicates the number of points to skip/remove from the beginning
of the curve.
If |
layout |
specification of the layout.
It may be a single numeric or a numeric couple, to indicate a square or rectangular layout
respectively, that is filled row by row.
It may also be a matrix that is directly passed to the function |
Rowv |
clustering specification(s) for the rows. It allows to specify the distance/clustering/ordering/display parameters to be used for the rows only. See section Row/column ordering and display for details on all supported values. |
main |
Main title as a character string or a grob. |
The methods compare
enables to compare multiple NMF fits either
passed as arguments or as a list of fits.
These methods eventually call the method summary,NMFList
, so that
all its arguments can be passed named in ...
.
summary,NMFList
computes summary measures for each NMF result in the list
and return them in rows in a data.frame
.
By default all the measures are included in the result, and NA
values
are used where no data is available or the measure does not apply to the
result object (e.g. the dispersion for single' NMF runs is not meaningful).
This method is very useful to compare and evaluate the performance of
different algorithms.
plot
plot on a single graph the residuals tracks for each fit in x
.
See function nmf
for details on how to enable the tracking of residuals.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | x <- rmatrix(20,10)
res <- nmf(x, 3)
res2 <- nmf(x, 2, 'lee')
# compare arguments
compare(res, res2, target=x)
# compare each fits in a multiple runs
res3 <- nmf(x, 2, nrun=3, .opt='k')
compare(res3)
compare(res3, res, res2)
compare(list(res3), res, res2, target=x)
# compare elements of a list
compare(list(res, res2), target=x)
|
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