dpih {KernSmooth}  R Documentation 
Uses direct plugin methodology to select the bin width of a histogram.
dpih(x, scalest="minim", level=2, gridsize=401, range.x=range(x), truncate=TRUE)
x 
vector containing the sample on which the histogram is to be constructed. 
scalest 
estimate of scale.
"stdev"  standard deviation is used.
"iqr"  interquartile range divided by 1.349 is used.
"minim"  minimum of "stdev" and "iqr" is used.

level 
number of levels of functional estimation used in the plugin rule. 
gridsize 
number of grid points used in the binned approximations to functional estimates. 
range.x 
range over which functional estimates are obtained. The default is the minimum and maximum data values. 
truncate 
if truncate is TRUE then observations outside
of the interval specified by range.x are omitted.
Otherwise, they are used to weight the extreme grid points.

The direct plugin approach, where unknown functionals that appear in expressions for the asymptotically optimal bin width and bandwidths are replaced by kernel estimates, is used. The normal distribution is used to provide an initial estimate.
the selected bin width.
This method for selecting the bin width of a histogram is described in Wand (1995). It is an extension of the normal scale rule of Scott (1979) and uses plugin ideas from bandwidth selection for kernel density estimation (e.g. Sheather and Jones, 1991).
Scott, D. W. (1979). On optimal and databased histograms. Biometrika, 66, 605–610.
Sheather, S. J. and Jones, M. C. (1991). A reliable databased bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B, 53, 683–690.
Wand, M. P. (1995). Databased choice of histogram binwidth. University of New South Wales, Australian Graduate School of Management Working Paper Series No. 95–011.
data(geyser, package="MASS") x < geyser$duration h < dpih(x) bins < seq(min(x)0.1, max(x)+0.1+h, by=h) hist(x, breaks=bins)