Description Usage Arguments Details Value Author(s) Examples
For a given column cur.ch
that belongs to a matrix, and a given number of cuts n
,
compute n1
bins using either fixed of combined limits
1  make.cut(mat, n = 5, count.lim = 40)

mat 
the matrix to cut 
n 
the number of cuts to generate (defaults to 5) 
count.lim 
the minimum number of counts to consider for density (defaults to 40) 
the fixed limits correspond to 5 equally spaced values over the range of the column.
the combined limits take the local minima and maxima determined using the localMinima
and localMaxima
functions, to adjust the limits using the following algorithm:
define d
as half the distance between 2 fixed limits
merge local minima and local maxima that are closer than d
if any fixed limit is closer to a local minima than d
, move the fixed limit to the local minima;
move the limits that are not been moved yet, and that are before and after the moved limit
so that they are evenly spread; repeat until no fixed limit can be moved
if some limits have been moved to a local minima, remove limits that are closer than d
to
a local maxima; move the limits that are not been moved yet, and that are before and after
the deleted limit so that they are evenly spread; repeat until no fixed limit can be moved
if no limits has been moved to a local minima, move limits that are closer than d
to
a local maxima; move the limits that are not been moved yet, and that are before and after
the moved limit so that they are evenly spread; repeat until no fixed limit can be moved
The function returns a list of lists, one for each column in mat
, consisting of
cur.dens
the density used to describe the data
cur.hist
the histogram used to describe the data
fixed
the fixed, evenly spaced cuts
minima
the local minima detected in the data
maxima
the local maxima detected in the data
combined
the cuts defined using a combination of fixed positions, local minima and local maxima
a list of of cuts for each column in mat
, see details
Yann Abraham
1 2 3 4 5 6 7 8 9 10 11 12  # generate a random 3D matrix with 2 peaks
mat < rbind(matrix(rnorm(300),ncol=3),
matrix(rnorm(300,5,1),ncol=3))
dimnames(mat)[[2]] < LETTERS[1:3]
# estimate the Hilbert order
hilbert.order(mat)
# generate 2 bins with a minimum bin size of 5
cuts < make.cut(mat,n=3,count.lim=5)
show.cut(cuts)
# Generate the cuts
cut.mat < do.cut(mat,cuts,type='fixed')
head(cut.mat)

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