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Fit all valid parametric probability distributions to data

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Fit all valid parametric probability distributions to data

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Mike Sheppard (view profile)

 

06 Feb 2012 (Updated )

ALLFITDIST Fit all valid parametric probability distributions to data.

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Description

ALLFITDIST Fit all valid parametric probability distributions to data.
[D PD] = ALLFITDIST(DATA) fits all valid parametric probability distributions to the data in vector DATA, and returns a struct D of fitted distributions and parameters and a struct of objects PD representing the fitted distributions. PD is an object in a class derived from the ProbDist class.
 
[...] = ALLFITDIST(DATA,SORTBY) returns the struct of valid distributions sorted by the parameter SORTBY
   NLogL - Negative of the log likelihood
   BIC - Bayesian information criterion (default)
   AIC - Akaike information criterion
   AICc - AIC with a correction for finite sample sizes
 
[...] = ALLFITDIST(...,'DISCRETE') specifies it is a discrete distribution and does not attempt to fit a continuous distribution to the data
 
[...] = ALLFITDIST(...,'PDF') or (...,'CDF') plots either the PDF or CDF of a subset of the fitted distribution. The distributions are plotted in order of fit, according to SORTBY.
 
List of distributions it will try to fit
  Continuous (default)
   Beta
   Birnbaum-Saunders
   Exponential
   Extreme value
   Gamma
   Generalized extreme value
   Generalized Pareto
   Inverse Gaussian
   Logistic
   Log-logistic
   Lognormal
   Nakagami
   Normal
   Rayleigh
   Rician
   t location-scale
   Weibull
 
  Discrete ('DISCRETE')
   Binomial
   Negative binomial
   Poisson
 
Optional inputs:
[...] = ALLFITDIST(...,'n',N,...)
For the 'binomial' distribution only:
'n' A positive integer specifying the N parameter (number of trials). Not allowed for other distributions. If 'n' is not given it is estimate by Method of Moments. If the estimated 'n' is negative then the maximum value of data will be used as the estimated value.
[...] = ALLFITDIST(...,'theta',THETA,...)
For the 'generalized pareto' distribution only:
'theta' The value of the THETA (threshold) parameter for the generalized Pareto distribution. Not allowed for other distributions. If 'theta' is not given it is estimated by the minimum value of the data.
 
Note: ALLFITDIST does not handle nonparametric kernel-smoothing, use FITDIST directly instead.
 
 
EXAMPLE 1
Given random data from an unknown continuous distribution, find the best distribution which fits that data, and plot the PDFs to compare graphically.
data = normrnd(5,3,1e4,1); %Assumed from unknown distribution
[D PD] = allfitdist(data,'PDF'); %Compute and plot results
D(1) %Show output from best fit
 
EXAMPLE 2
Given random data from a discrete unknown distribution, with frequency data, find the best discrete distribution which would fit that data, sorted by 'NLogL', and plot the PDFs to compare graphically.
data = nbinrnd(20,.3,1e4,1);
values=unique(data); freq=histc(data,values);
[D PD] = allfitdist(values,'NLogL','frequency',freq,'PDF','DISCRETE');
PD{1}
 
EXAMPLE 3
Although the Geometric Distribution is not listed, it is a special case of fitting the more general Negative Binomial Distribution. The parameter 'r' should be close to 1. Show by example.
data=geornd(.7,1e4,1); %Random from Geometric
[D PD]= allfitdist(data,'PDF','DISCRETE');
PD{1}
 
EXAMPLE 4
Compare the resulting distributions under two different assumptions of discrete data. The first, that it is known to be derived from a Binomial Distribution with known 'n'. The second, that it may be Binomial but 'n' is unknown and should be estimated. Note the second scenario may not yield a Binomial Distribution as the best fit, if 'n' is estimated incorrectly. (Best to run example a couple times to see effect)
data = binornd(10,.3,1e2,1);
[D1 PD1] = allfitdist(data,'n',10,'DISCRETE','PDF'); %Force binomial
[D2 PD2] = allfitdist(data,'DISCRETE','PDF'); %May be binomial
PD1{1}, PD2{1} %Compare distributions
 

Required Products Statistics and Machine Learning Toolbox
MATLAB release MATLAB 7.12 (R2011a)
Other requirements Note: Requires Statistics Toolbox
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Comments and Ratings (34)
29 Jul 2015 tafteh

tafteh (view profile)

Hi Mike,

Thanks for your brilliant job in this script.

However I came across one weird results:
The probability Density Function plot produces the y-axis scaled from 0 to 2.5, and the peak of the fitted distributions are going high up to "2." Is it right?

I would appreciate any help,
Thanks,

21 Jul 2015 Danilo Gaspar

Very useful script.

Hi Abdullahi Salman, to show the output results properly you should index the variable, as shown by the example, D(1).

23 Jun 2015 Abdullahi Salman

Awesome script. Am however having a little problem. D and PD are not outputting any result. I can see the plot of the pdf though. this is what am getting:

D =
1x6 struct array with fields:
DistName
NLogL
BIC
AIC
AICc
ParamNames
ParamDescription
Params
Paramci
ParamCov
Support

PD =

Columns 1 through 5

[1x1 ProbDistUnivParam] [1x1 ProbDistUnivParam] [1x1 ProbDistUnivParam] [1x1 ProbDistUnivParam] [1x1 ProbDistUnivParam]

Column 6

[1x1 ProbDistUnivParam]

I will appreciate any help. Thank you.

16 Jun 2015 Anshul Goyal  
07 Apr 2015 Vassilios Vonikakis

very easy and direct to use

10 Mar 2015 John Knag

Super easy to use and very helpful. Thank you.

20 Feb 2015 Roudy DAGHER

Hi Mike,

that's a very nice script.
It would be also useful to test against mixtures, for instance when the data can be fit to a mixture of two or more gaussians, with the parameter k increasing...
see fitgmdist Matlab function.

Best,
Roudy

20 Jan 2015 SANHANAT

best MATLAB code so far

22 Aug 2014 Alireza

The allfitdist function for normally distributed data return 'rayleigh' as best fit distribution! So weird as it is an example included in file.

commands: data = normrnd(5,3,1e4,1); [D PD] = allfitdist(data,'PDF'); D(1)

output: ans =

DistName: 'rayleigh'
NLogL: 2.4515e+04 - 1.5959e+03i
BIC: 4.9038e+04 - 3.1919e+03i
AIC: 4.9031e+04 - 3.1919e+03i
AICc: 4.9031e+04 - 3.1919e+03i
ParamNames: {'B'}
ParamDescription: {'scale'}
Params: 4.1166
Paramci: [2x1 double]
ParamCov: 4.2366e-04
Support: [1x1 struct]

Comment only
02 Jul 2014 Nebitno  
28 Jan 2014 sonakis23 sonaki

Hi, I was wondering how could I plot both PDF, CDF and the error graph any ideas?

Comment only
26 Nov 2013 debora

debora (view profile)

@Hernando

I've the same problem. You need to change all ~ (line 245 and others) by another letter.

25 Nov 2013 Hernando

Well i`m using r2009a. and using the file i've got this error:
??? Error: File: allfitdist.m Line: 245 Column: 11
Expression or statement is incorrect--possibly unbalanced (, {, or [.

[D PD] = allfitdist(data,'CCDF');
??? Error: File: allfitdist.m Line: 245 Column: 11
Expression or statement is incorrect--possibly unbalanced (, {, or [.
data = normrnd(5,3,1e4,1);
>> [D PD] = allfitdist(data,'CCDF');
??? Undefined function or method 'allfitdist' for input arguments of type 'double'.
Is there any restriction for the file?

Comment only
16 Sep 2013 Venkatesh

Very useful script

14 Aug 2013 Shebuti Rayana

I am using Matlab R2008a version I am trying to use this code but its not working Its showing no distributions were found for the example no 1. I checked my matlab version and it contains Statistics toolbox. Now what should I do. Please help.

Comment only
09 Jul 2013 katmai46

Dear Mr. Sheppard,

I have been used your code to fit several datasets that I have. I found it really useful. My question is (I am very new in Matlab as well as statistics)... how do you define the "best" distribution? Based on p-values of KSTest?
Thanks

22 Aug 2012 Manuel Kuhs

Really appreciate your function, was doing this manually for a while!

I apologise in advance if this is an ignorant question, as I'm a very basic MatLab user.

Would it be possible to amend your script to take into account for situations in which you know some data is missing? The particular type I'm interested in is when I know that my data actually only represents e.g. the first 70% on the CDF.

I hope this question makes sense. I'm not even sure of the right terminology to use!

03 May 2012 Nitin

Nitin (view profile)

 
25 Mar 2012 Olga Petrik  
15 Mar 2012 Roni Peer

Roni Peer (view profile)

Great Job.
I've changed it a bit to suit my needs, and going to add a GUI to allow the user to fit just a specific distribution, or select some of them. ALL of them would be a default.
Thanks!

13 Mar 2012 Mike Sheppard

Mike Sheppard (view profile)

Hi Roni,

The "Best Fit" can be found by the output by either D(1) or PD{1}, depending on if you want a structure or ProbDist class object. You can use the class object directly in other statistical functions, such as:

p=cdf(PD{1},xvalue)

The reason for including all valid distributions is that depending on preferences of model selection or assumptions from the data the distribution that you may prefer to use may be the 2nd or even 3rd "best" from the output, or not given at all. This is especially true if the SORTBY values are close in value, or if a parameter in a given distribution is close to a simpler special case.

Example 3 is an example of the latter; should you use as a model the Negative Binomial Distribution with r=.98 or assume it is actually the more simpler Geometric Distribution with r=1 which is not given as an output?

The error graph is displayed when 'CDF' is given as an input. You can change the number of distributions to include in the plot by adjusting the max_num_dist variable in the plotfigs subfunction.

Hope that helps,

-MIke

Comment only
13 Mar 2012 Roni Peer

Roni Peer (view profile)

Hi Mike,

Why not add a "Best Fit" output also?
For example, if the best distribution which represents this data is "Weibull", return it as another output.
This can be used to find "Best Fit" for this data, which can be really useful.
I would also add a summary graph, which shows the error on all types of distributions, and what was the best one.

Roni.

Comment only
07 Mar 2012 Eric Diaz

Very useful indeed!

28 Feb 2012 Francesco Cosentino

Hi Guys,

the problem at lines 247 etc is resolved by replacing the tilde operator with any name for a variable that will remain unused, but for the problem that also Olivier noted, this is due to the fact that function fitdist is missing in matlab 7.7

Regards
Francesco

Comment only
28 Feb 2012 Francesco Cosentino

Hi people,

This script is not working on matlab 7.7.

Matlab recognises an error in the code at line 247. It says:

Parse error at ',': usage might be invalid matlab syntax
Parse error at ']': usage might be invalid matlab syntax

And the error is repeated for lines 249 249 251 253.

Is there any way of getting it working on 7.7???

Regards
Francesco

Comment only
15 Feb 2012 Mike Sheppard

Mike Sheppard (view profile)

Warwick, thanks for your note. I am updating the file a bit, and the functionality of custom distributions seems interesting.

If you like, you can e-mail me directly with your improved functionality and I can include it in the next update with acknowledgment.

Comment only
14 Feb 2012 Warwick

Mike, I am sorry and aghast about the rating. I actually meant to leave the rating blank. On further experiment, there seems to be no way to go back to a null rating once my cursor merely touches the rating banner of stars (using iMac and the beta R2012a) . Anyway, I was able to use the file to obtain sorted best-fit curves on the type of problems I have and even added custom dist.

14 Feb 2012 Mike Sheppard

Mike Sheppard (view profile)

Warwick, for a "potentially a very useful script" I'm sorry you felt it was only worth a rating of one. Do you have suggestions on how it can be improved? Constructive criticism or ways to improve the program/functionality are always welcome, but I did not see any in your comment, other than asking for specific help after giving it a poor rating.

Please re-read the help section; specifically Example 2.

Comment only
14 Feb 2012 Warwick

Mike, this is potentially a very useful script for me. How can I use it for this example problem? I have frequency data describing number of events against day number. Logically the day number must be an integer from 1.
Eg, for discrete days 1:10 and the Yobs are [1099 478 263 159 99 64 41 28 18 12]. Exponential and Weibull are fair candidate distrubution and I have previously fitted these as curves using LS or weighted LS, but an MLE approach ( ie, use neg log likelihood) would be much better as there can be a lot of noise in the tails. Thanks, Warwick

13 Feb 2012 Jiro Doke

Jiro Doke (view profile)

Olivier,

Do you have Statistics Toolbox? It's required to use this function.

Comment only
12 Feb 2012 Tony Dalton  
10 Feb 2012 Matthew

Great idea, good examples, functional code (style could be better).

10 Feb 2012 Olivier Planchon

Does not work on Matlab 7.7
(Or I misunderstood how to use it)

>> [D, PD] = allfitdist(randn(1000,1)) ;
??? Error using ==> allfitdist at 238
No distributions were found

Comment only
07 Feb 2012 Jonathan Sullivan  
Updates
07 Feb 2012 1.1

Included error checking for NaNs in data set and/or frequency; and dimension mismatch between data and frequency

07 Feb 2012 1.2

Corrected y-axis labels

17 Feb 2012 1.3

Fixed frequency data with binomial; generalized pareto as special case; and cleaned up code

04 Apr 2012 1.4

Updated help section

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