Vectors

Let us finish with a third type of objects, that are vectors, which contains slighty more methods in addition to those already presented in the histogram_tutorial_ <test> and Convolution <convolution_tutorial_> sections

Constructor

Again, Vectors can be generated either by loading an ASCII or directly using python lists as follows:

>>> v1 = Vectors('./test/data/chene_sessile.vec')
>>> v2 = Vectors([[1,2], [3,4]])

Note

Note the syntax, which is a list of lists

Then, you can access to various information using:

>>> v2.nb_variable
2
>>> v2.nb_vector
2
>>> v2.get_identifiers()
[1, 2]

Finally, container are available and you can access to the data as follows (starting at 0):

>>> v2[1]
[3, 4]
>>> v2[1][0]
3

Display, Save, str() methods are available as in the previous cases.

However, there is no plotting routines available.

There are many more methods available, some of them are explained here below

VarianceAnalysis

Here is the usage of One-way variance analysis.

>>> print VarianceAnalysis(v2, 1,2,"O")
value                          1   3
sample size                    1   1
mean                           2   4
variance                       0   0
standard deviation             0   0
mean absolute deviation        0   0
coefficient of concentration   1   1
coefficient of skewness        0   0
coefficient of kurtosis       -2  -2

   | frequency distribution 1 | frequency distribution 3 | cumulative distribution 1 function | cumulative distribution 3 function
0  0  0  0  0
1  0  0  0  0
2  1  0  1  0
3     0     0
4     1     1

Kruskal-Wallis test
chi-square test (1 degree of freedom)
chi-square value: 1   critical probability: 0.315013
reference chi-square value: 3.74866   reference critical probability: 0.05

Compare

>>> print Compare(ExtractHistogram(v2, 1), ExtractHistogram(v2,2), "O")
frequency distribution 1 - sample size: 2
mean: 2   variance: 2   standard deviation: 1.41421
coefficient of skewness: 0   coefficient of kurtosis: -2.5
mean absolute deviation: 1   coefficient of concentration: 0.25
information: -1.38629 (-0.693147)

frequency distribution 2 - sample size: 2
mean: 3   variance: 2   standard deviation: 1.41421
coefficient of skewness: 0   coefficient of kurtosis: -2.5
mean absolute deviation: 1   coefficient of concentration: 0.166667
information: -1.38629 (-0.693147)

   | frequency distribution 1 | frequency distribution 2 | cumulative distribution 1 function | cumulative distribution 2 function
0  0  0    0    0
1  1  0  0.5    0
2  0  1  0.5  0.5
3  1  0    1  0.5
4     1         1

dissimilarities between frequency distributions

            | frequency distribution 1 | frequency distribution 2
frequency distribution 1      0   0.5
frequency distribution 2   -0.5     0

Kruskal-Wallis test
chi-square test (1 degree of freedom)
chi-square value: 0.6   critical probability: 0.448429
reference chi-square value: 3.74866   reference critical probability: 0.05

ContingencyTable

>>> print ContingencyTable(v2, 1, 2)
contingency table

   2  3  4
1  1  0  0  1
2  0  0  0  0
3  0  0  1  1
   1  0  1  2

deviation table

      2     3     4
1   0.5     0  -0.5
2     0     0     0
3  -0.5     0   0.5

chi-square contribution table

      2     3     4
1  0.25     0  0.25
2     0     0     0
3  0.25     0  0.25

chi-square test (1 degree of freedom)
chi-square value: 2   critical probability: 0.160475
reference chi-square value: 3.74866   reference critical probability: 0.05