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SCaVis manual

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Statistics

The package jhplot.stat can be used for descriptive analysis of random distributions. Similarly, cern.jet.stat.Descriptive package contains descriptive methods to calculate many statistical characteristics.

Consider also several other packages:

But before using such packages, check again the data containers such as P1D or H1D. They already have many useful methods to access statistical information on data.

Some statistical questions have been already described in the Section random_numbers. Below we will consider several advances topics.

Moments of a distribution

SCaVis can be used to determine statistical characteristics of an arbitrary frequency distribution. Moments of this distribution can be calculated up to the 6th order. Read more here.

Code example

  Download for this example is disabled for non-members
1: from jhplot  import *
2: from jhplot.math.StatisticSample import *
3:
4: a=randomLogNormal(1000,0,10) # generate random 1000 numbers between 0 and 10 using a LogNormal distribution
5: p0=P0D(a)                    # convert it to an array
6: print p0.getStatString()     # print detailed characteristics

Run this script and you will get a very detailed information about this distribution (rather self-explanatory)

Click here to see the output of this script

Click here to see the output of this script

Click here to see the output of this script

Size: 1000
Sum: 2.0795326321690155E11
SumOfSquares: 1.722072831288292E22
Min: 4.3681673233597326E-14
Max: 1.187289072883721E11
Mean: 2.0795326321690154E8
RMS: 4.1497865382309628E9
Variance: 1.7194678431631995E19
Standard deviation: 4.14664664899627E9
Standard error: 1.3112848062732975E8
Geometric mean: 0.7193930848008395
Product: 9.252494313364321E-144
Harmonic mean: 2.2976022239249118E-11
Sum of inversions: 4.352363475222163E13
Skew: 25.65476598759878
Kurtosis: 694.7699433878664
Sum of powers(3): 1.839916709064571E33
Sum of powers(4): 2.0782654881247146E44
Sum of powers(5): 2.4093597349729484E55
Sum of powers(6): 2.8286717081193334E66
Moment(0,0): 1.0
Moment(1,0): 2.0795326321690154E8
Moment(2,0): 1.722072831288292E19
Moment(3,0): 1.839916709064571E30
Moment(4,0): 2.0782654881247147E41
Moment(5,0): 2.409359734972948E52
Moment(6,0): 2.8286717081193336E63
Moment(0,mean()): 1.0
Moment(1,mean()): 4.931390285491944E-7
Moment(2,mean()): 1.7177483753200437E19
Moment(3,mean()): 1.8291913748162454E30
Moment(4,mean()): 2.0630054468429083E41
Moment(5,mean()): 2.3878300421487077E52
Moment(6,mean()): 2.798744135044988E63
25%, 50%, 75% Quantiles: 0.0012310644573427145, 0.9530465118707188, 535.0653267374155
quantileInverse(median): 0.5005
Distinct elements & frequencies not printed (too many).

One can access all such values using the method “getStat()” which returns a Java Map (or Jython dictionary) with the key representing statistical characteristics of this array.

You can also visualize the random numbers in the form of a histogram:

example.py
from jhplot  import * 
from jhplot.math.StatisticSample import *
a=randomLogNormal(1000,0,10)
 
h=H1D("LogNormal",40,-10,10)  # make a histogram
h.fill(a)
 
c1 = HPlot()
c1.setGTitle("LogNormal");
c1.visible()
c1.setAutoRange()
c1.draw(h)

Statistics with P1D

You can get detailed statistics on P1D data using the method getStat(axis), where axis=0 for X and axis=1 for Y. It returns a map (for JAVA) or Python dictionary (for Jython) where each statistical characteristics can be accessed using a key, such as mean, RMS, variance, error on the mean at. Assuming that P1D is represented by “p1” object, try this code:

stat=p2.getStat(0)  # get PYTHON dictionary with statistics for X
for key in  stat: 
     print key , 't', stat[key]

This will print the following values:

Click here to see the output of this script

Click here to see the output of this script

Click here to see the output of this script

error     0.996592835069
rms     5.05682000584
mean     4.42857142857
variance     6.95238095238
stddev     2.63673679998

Statistical tests

Two distributions (1D and 2D histograms, P1D data points) can be compared by applying several statistical tests. The following statistical comparisons are available

  • Chi2
  • Anderson-Darling
  • Kolmogorov-Smirnov
  • Goodman
  • Kuiper
  • Tiku

Consider a simple statistical test: compare 2 histograms. You can generate 2 similar histograms using this code snippet:

from java.awt import Color
from java.util import Random
from jhplot  import *
 
c1 = HPlotJa("Canvas")
c1.setGTitle("Statistical comparisons")
c1.visible()
c1.setAutoRange()
 
h1 = H1D("Histo1",20, -2, 2.0)
h1.setColor(Color.blue)
h2 = H1D("Histo2",20, -2, 2.0)
r = Random()
for i in range(10000):
   h1.fill(r.nextGaussian())
   h2.fill(r.nextGaussian())
   if (i<100): h2.fill(2*r.nextGaussian()+2)
h1.setErrAll(1)
h2.setErrAll(0)
c1.draw(h1)
c1.draw(h2)

Here we show statistical uncertainties only for the first (blue) histogram (see the method setErrAll(0)). The output of this code is shown below

Click here to see the output of this script

Click here to see the output of this script

Click here to see the output of this script

 Two similar histograms

Now we can perform a several tests to calculate the degree of similarity of these distributions (including their uncertainties). Below we show a code which compares these two histograms and calculate Chi2 per degree of freedom:

Unregistered users have a limited access to this section. One can unlock this example after becoming a full member.

The output of this script is shown here:

AndersonDarling method= 2.21779532164 / 20
Chi2 method= 0.786556311893 / 20
Goodman method= 0.624205522632 / 20
KolmogorovSmirnov  method= 0.419524135727 / 20

Linear regression analysis

Snippet from Wikipedia: Linear regression

In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.

There are several tools to perform linear regressions and estimate slope and intercept (with statistical uncertainties), as well as to estimate the prediction and confidence level bands. Let us make a short example generating data in 2D and performing linear regression fit:

Code example

  Download for this example is disabled for non-members
 1: from jhplot  import *
 2: from jhplot.stat  import LinReg
 3: from java.awt import Color
 4: from java.util import Random
 5:
 6: c1 = HPlot("Linear regression")
 7: c1.visible()
 8: c1.setGTitle("Linear regression")
 9: c1.setAutoRange()
10:
11: p1= P1D("data")
12: rand = Random()
13: for i in range(200):
14:           x=rand.nextGaussian()
15:           y=rand.nextGaussian()
16:           p1.add(0.2*x, y)
17: c1.draw(p1)
18:
19: r = LinReg(p1)
20: print "Intercept=",r.getIntercept(), "+/-",r.getInterceptError()
21: print "Slope=",r.getSlope(),"+/-",r.getSlopeError()
22:
23: pP=r.getPredictionBand(Color.red,0.5) # get prediction and show as a band
24: c1.draw(pP)
25:
26: c1.draw(  r.getResult()  )     # draw F1D function representing the fit result
27: c1.draw(  r.getPrediction() )  # draw the prediction interval

The output of this script is the fit values:

Intercept= 0.0637647629564 +/- 0.0656878315703
Slope= 0.1024396794 +/- 0.331874315937

Distribution functions

Many useful distribution functions can be found in cern.jet.stat.Probability package. The package contains numerical integration of certain probability distributions. Below we will show how to use the normal distribution which is very useful distribution in many statistical analyses.

In the example below we will compute the probability that our random outcome is within a specified interval using the normal distribution.

The code below returns the area under the normal probability density function, integrated from minus infinity to -1.17 (assumes mean is zero, variance is one).

from cern.jet.stat.Probability import *
print normal(-1.17)

For the two-sided case, one can multiply the result by 2.

Unregistered users have a limited access to this section. One can unlock this example after becoming a full member.

Normalised Factorial Moments

Normalized factorial moments (NFM) are used to measure deviations of a multiplicity distribution from a Poissonian case. As example, let us consider calculations of normalised factorial moments (NFM) for several distributions. They are defined as

<m 14>F_q = {<n (n-1) .. (n+1-q)>}/{ <n>^{q}}</m>.

where “n” is a random number (integer) number. According to this definition, a Poisson distribution has all moments equal to 1. A broader than a Poisson distribution have moments larger then one. Let us calculate the NFM up to 4th order for a Poisson distribution Binomial and a Negative-binomial distributions

Code example

  Download for this example is disabled for non-members
 1: from jhplot  import *
 2: from jhplot.stat  import MomentsFacNorm
 3: from cern.jet.random.engine import *
 4: from cern.jet.random  import *
 5: from jhplot.shapes  import Line
 6: from java.awt import Color
 7:
 8: c1 = HPlot("Canvas")
 9: c1.visible(1)
10: c1.setNameX("NFM order")
11: c1.setNameY("Values")
12: c1.setRange(0,5,0,2)
13:
14: line = Line(0.0,1, 5., 1.) # draw  a vertical line in the NDC system
15: line.setPosCoord("USER"); line.setColor(Color.gray); line.setTransparency(0.5)
16: c1.add(line)
17:
18: # build a random engine
19: engine=MersenneTwister()
20:
21: poisson=Poisson(10,engine) # a Possonian distribution
22: m=MomentsFacNorm(4)        # calculates moments up to 4th order
23:
24: for i in range(100):
25:         m.process( poisson.nextInt())
26: p1=m.getResults()
27: p1.setTitle("NFM for Poissson"); p1.setSymbol(4); p1.setSymbolSize(10)
28: c1.draw(p1)
29: print(p1.toString())
30:
31: binomial=Binomial(10, 0.2, engine)  # Binomial distribution
32: m=MomentsFacNorm(4) # calculates moments up to 4th order
33: for i in range(200):
34:        m.process( binomial.nextInt())
35: p2=m.getResults(); p2.setTitle("NFM for Binomial"); p2.setSymbol(5); p2.setSymbolSize(10)
36: c1.draw(p2)
37: print(p2.toString())
38:
39: nbinom=NegativeBinomial(10, 0.4, engine) # NegativeBinomial distribution (NBD)
40: m=MomentsFacNorm(4)
41: for i in range(300):
42:        m.process( nbinom.nextInt())
43: p3=m.getResults(); p3.setTitle("NFM for NBD"); p3.setSymbol(6); p3.setSymbolSize(10)
44: c1.draw(p3)
45: print(p3.toString())

The output file shows the NFM for all three distributions together with statistical errors.

Correlation coefficients

Correlation coefficients between two Python lists can can be obtained using several methods.

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Setting limits

This section describes how to set a limit on observation of a signal in the presence of a background distribution. We will also consider a situation when the background is affected by a systematic uncertainty. We will consider how to estimate statistical significance of an observation in presence of statistical and systematic al errors (in case of observation) and also how to set the 95% CL exclusion limit (in case of no observation). We will show how to estimate 95% confidence limit with correct treatment of statistical errors.

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A complete description of how to use Java, Jython and SCaVis for scientific analysis is described in the book Scientific data analysis using Jython and Java published by Springer Verlag, London, 2010 (by S.V.Chekanov)

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man/stat/statistics.1370038297.txt.gz · Last modified: 2013/07/04 21:52 (external edit)
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