Given a vector, or a matrix with 1, 2 or 3 columns, this function constructs a frequency table
associated with appropriate intervals covering the range of x
.
binning(x, y, breaks, nbins)
In the vector case, a list is returned containing the following elements:
a vector x
of the midpoints of the bins excluding those with 0 frequecies,
its associated matrix x.freq
of frequencies, the co-ordinates of the
midpoints
, the division points, and the complete vector of observed
frequencies freq.table
(including the 0 frequencies), and the vector
breaks
of division points.
In the matrix case, the returned value is a list with the following
elements: a two-dimensional matrix x
with the coordinates of the
midpoints of the two-dimensional bins excluding those with 0 frequencies,
its associated matrix x.freq
of frequencies, the coordinates of the
midpoints
, the matrix breaks
of division points, and the observed
frequencies freq.table
in full tabular form.
a vector or a matrix with either one, two or three columns, containing the original data.
a vector of data, for example response data, associated with the data in x
.
either a vector or a matrix with two columns (depending on the dimension of x
),
assigning the division points of the axis, or the axes in the matrix case.
It must not include Inf
,-Inf
or NA
s, and it must span the whole range of
the x
points.
If breaks
is not given, it is computed by dividing the range of x
into nbins
intervals for each of the axes.
the number of intervals on each axis. If nbins
is not supplied, a value is computed as round(log(n)/log(2) + 1)
.
This function is called automatically (under the default settings)
by some of the functions of the sm
library when the sample size is
large, to allow handling of datasets of essentially unlimited size.
Specifically, it is used by sm.density
, sm.regression
, sm.ancova
,
sm.binomial
and sm.poisson
.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
# example of 1-d use
x <- rnorm(1000)
xb <- binning(x)
xb <- binning(x, breaks=seq(-4,4,by=0.5))
# example of 2-d use
x <- rnorm(1000)
y <- 2*x + 0.5*rnorm(1000)
x <- cbind(x, y)
xb<- binning(x, nbins=12)
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