Compute the number of classes for a histogram.
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a data vector.
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
nclass.FD uses the Freedman-Diaconis choice based on the
inter-quartile range (
IQR(signif(x, 5))) unless that's
zero where it uses increasingly more extreme symmetric quantiles up to
c(1,511)/512 and if that difference is still zero, reverts to using
The suggested number of classes.
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.
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