nclass {graphics}R Documentation

Compute the Number of Classes for a Histogram

Description

Compute the number of classes for a histogram, for use internally in hist.

Usage

nclass.Sturges(x)
nclass.scott(x)
nclass.FD(x)

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.

nclass.FD uses the Freedman-Diaconis choice based on the inter-quartile range.

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.

See Also

hist


[Package graphics version 2.1.0 Index]