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

 `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.

`hist` and `truehist` (which use a different default).

### Examples

```set.seed(1)
x <- 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 2.5.0 Index]