sm: binning – R documentation

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binning

Construct frequency table from raw data

Description

Given a vector or a matrix x, this function constructs a frequency table associated to appropriate intervals covering the range of x.

Usage

binning(x, y, breaks, nbins)

Arguments

`x, y`	a vector or a matrix with either one or two columns. If `x` is a one-dimentional matrix, this is equivalent to a vector.
`breaks`	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.
`nbins`	the number of intervals on the `x` axis (in the vector case), or a vector of two elements with the number of intervals on each axes of `x` (in the matrix case). If `nbins` is not given, a value is computed as `round(log(length(x))/log(2)+1)` or using a similar expression in the matrix case.

Details

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.

Value

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 coodinateds 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 frequecies, 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.

References

Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.

Examples

# 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)

sm

Smoothing Methods for Nonparametric Regression and Density Estimation

v2.2-5.6

GPL (>= 2)

Authors

Adrian Bowman and Adelchi Azzalini. Ported to R by B. D. Ripley <ripley@stats.ox.ac.uk> up to version 2.0, version 2.1 by Adrian Bowman and Adelchi Azzalini, version 2.2 by Adrian Bowman.

Initial release

2018-09-27