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stats/
optbins.pro
Routines
result = post_prob_optbins(M)result = optbins(data [, minM_in] [, maxM] [, HIST=HIST] [, PLOT=PLOT] [, _EXTRA=_EXTRA])calculate the optimum number of histogram bins by balancing the likelihood function and the prior probability of the model.
Routine details
toppost_prob_optbins
result = post_prob_optbins(M)
Parameters
- M
Statistics
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topoptbins
stats
result = optbins(data [, minM_in] [, maxM] [, HIST=HIST] [, PLOT=PLOT] [, _EXTRA=_EXTRA])
calculate the optimum number of histogram bins by balancing the likelihood function and the prior probability of the model. Method is from Optimal Data-Based Binning for Histograms Kevin H. Knuth arXiv:physics/0605197v1 [physics.data-an] 23 May 2006
FUNC_optbins, data2, N, minM, hist2
This is not generalized to 2-d histograms yet
Return value
optimal number of histogram bins
Parameters
- data in required
the array to find the optimal number of histogram bins for
- minM_in in optional
minimum number of bins to consider (default 5)
- maxM in optional
maximum number of bins to consider (default 100)
Keywords
- HIST in optional
the histogram of the data
- PLOT in optional
Use David Fannings Histoplot to plot the histogram
- _EXTRA in optional
_strict_extra keywords to histoplot
Examples
IDL> print, optbins(randomn(seed, 1000), /plot)
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Author information
- History:
Sun Jul 27 15:46:01 2008, Brian Larsen written and tested
Statistics
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| McCabe complexity: |
File attributes
| Modifcation date: | Mon Sep 22 12:41:16 2008 |
| Lines: | 104 |