Vectors#

An object with class Vectors is a collection of vectors, that is to say of containers for floating or integer values with fixed dimensions.

Constructor#

As for most other objects, Vectors can be generated either by loading an ASCII or directly using python lists as follows:

>>> v1 = Vectors(get_shared_data("chene_sessile.vec"))
>>> v2 = Vectors([[1,2], [3,4], [4,5]])

Note

Note the syntax, which is a list of lists. The inner elements of the list correspond to the vectors, which consist here in a collection of 3 vectors of dimension 2.

Then, you can access to various information using:

>>> v2.nb_variable
2
>>> v2.nb_vector
3
>>> v2.get_identifiers()
[1, 2, 3]

Identifiers are the indices of each vector.

Eventually, Vectors are containers so you can access the data (starting at index 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 which 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