nclass {grDevices} | R Documentation |

## Compute the Number of Classes for a Histogram

### Description

Compute the number of classes for a histogram.

### Usage

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

### Arguments

### 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(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
Scott's choice.

### 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

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

[Package

*grDevices* version 3.5.0

Index]