Compute the Number of Classes for a Histogram

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Description

Compute the number of classes for a histogram.

Usage

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Arguments

x

A data vector.

Details

nclass.Sturges uses Sturges' formula, implicitly basing bin sizes on the range of the data.

nclass.scott uses Scott's choice for a normal distribution based on the estimate of the standard error, unless that is zero where it returns 1.

nclass.FD uses the Freedman-Diaconis choice based on the inter-quartile range (IQR) unless that's zero where it reverts to mad(x, constant = 2) and when that is 0 as well, returns 1.

Value

The suggested number of classes.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S-PLUS. Springer, page 112.

Freedman, D. and Diaconis, P. (1981) On the histogram as a density estimator: L_2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 57, 453–476.

Scott, D. W. (1979) On optimal and data-based histograms. Biometrika 66, 605–610.

Scott, D. W. (1992) Multivariate Density Estimation. Theory, Practice, and Visualization. Wiley.

Sturges, H. A. (1926) The choice of a class interval. Journal of the American Statistical Association 21, 65–66.

See Also

hist and truehist (package MASS); dpih (package KernSmooth) for a plugin bandwidth proposed by Wand(1995).

Examples

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set.seed(1)
x <- stats::rnorm(1111)
nclass.Sturges(x)

## Compare them:
NC <- function(x) c(Sturges = nclass.Sturges(x),
      Scott = nclass.scott(x), FD = nclass.FD(x))
NC(x)
onePt <- rep(1, 11)
NC(onePt) # no longer gives NaN

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