
Compute the number of classes for a histogram.
nclass.Sturges(x)
nclass.scott(x)
nclass.FD(x)
a data vector.
The suggested number of classes.
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.
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:
Scott, D. W. (1979). On optimal and data-based histograms. Biometrika, 66, 605--610. 10.2307/2335182.
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. 10.1080/01621459.1926.10502161.
hist
and truehist
(package
MASS); dpih
(package
KernSmooth) for a plugin bandwidth proposed by Wand(1995).
# NOT RUN {
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
# }
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