R: Compute the Number of Classes for a Histogram

nclass {base} | 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.

### Note

For consistency with earlier versions of R, `nclass.Sturges` rounds down. This is incompatible with S-PLUS, and probably wrong: however the other algorithms are to be preferred.

### References

Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. Springer, pages 118–9.

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`

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