discrimintools.summaryGFA#

discrimintools.summaryGFA(obj, digits=4, nb_element=10, ncp=3, to_markdown=False, tablefmt='github', **kwargs)[source]#

Printing summaries of General Factor Analysis model.

Parameters:

  • obj (class) – an object of class GFA.

  • digits (int, default = 4) – The number of decimal printed.

  • nb_element (int,, default = 10.) – Number of element.

  • ncp (int,, default = 3.) – Number of components.

  • to_markdown (bool, default = False) – To print summaries in markdown-friendly format. Requires the tabulate. package.

  • tablefmt (str, default = “github”) – The table format.

  • **kwargs – additionals parameters. These parameters will be passed to tabulate.

Return type:

NoneType

See also

summaryGFALDA

Printing summaries of General Factor Analysis Linear Discriminant Analysis model.

summaryMPCA

Printing summaries of Mixed Principal Component Analysis model.

summaryMDA

Printing summaries of Mixed Discriminant Analysis model.

Examples

>>> from discrimintools.datasets import load_alcools, load_canines, load_heart
>>> from discrimintools import GFA, summaryGFA

The GFA performs principal component analysis (PCA) …

>>> #PCA
>>> D = load_alcools("train")
>>> X = D.drop(columns=["TYPE"])
>>> clf = GFA()
>>> clf.fit(X)
GFA()
>>> summaryGFA(clf)
                    General Factor Analysis - Results
Eigenvalues informations:
                         Can1     Can2     Can3     Can4     Can5     Can6     Can7      Can8
Variance               2.7988   1.7188   1.4034   1.0444   0.5368   0.2024   0.1851    0.1103
Difference             1.0799   0.3154   0.3590   0.5076   0.3344   0.0173   0.0748       NaN
% of var.             34.9848  21.4856  17.5428  13.0554   6.7101   2.5295   2.3136    1.3782
Cumulative % of var.  34.9848  56.4703  74.0132  87.0686  93.7786  96.3081  98.6218  100.0000
Individuals (the 10 first):
     Can1    Can2
0 -1.4901 -1.1150
1 -0.8484  1.0139
2 -1.7262  0.6568
3 -1.7259 -1.1717
4 -3.6258 -1.4189
5 -0.9469  2.3607
6 -0.7407  1.8183
7 -3.4476 -1.4497
8 -1.6847 -0.9451
9 -3.7593 -1.0701
Variables:
        Can1    Can2
MEOH  0.8711  0.0601
ACET  0.1017  0.4556
BU1   0.7630 -0.0353
BU2  -0.0493  0.7563
ISOP  0.7692  0.0130
MEPR  0.8078  0.1149
PRO1 -0.3263  0.8465
ACAL  0.3073  0.4522

multiple correspondence analysis (MCA)…

>>> #MCA
>>> D = load_canines("train")
>>> X = D.drop(columns=["Fonction"])
>>> clf = GFA()
>>> clf.fit(X)
GFA()
>>> summaryGFA(clf)
                    General Factor Analysis - Results
Eigenvalues informations:
                         Can1     Can2     Can3     Can4  ...     Can7     Can8     Can9     Can10
Variance               0.4816   0.3847   0.2110   0.1576  ...   0.0815   0.0457   0.0235    0.0077
Difference             0.0969   0.1738   0.0534   0.0074  ...   0.0358   0.0221   0.0158       NaN
% of var.             28.8964  23.0842  12.6572   9.4532  ...   4.8877   2.7402   1.4125    0.4628
Cumulative % of var.  28.8964  51.9806  64.6379  74.0911  ...  95.3845  98.1247  99.5372  100.0000
[4 rows x 10 columns]
Individuals (the 10 first):
               Can1    Can2
Beauceron   -0.3172 -0.4177
Basset       0.2541  1.1012
Berger All  -0.4864 -0.4644
Boxer        0.4474 -0.8818
Bull-Dog     1.0134  0.5499
Bull-Mastif -0.7526  0.5469
Caniche      0.9123 -0.0162
Chihuahua    0.8408  0.8439
Cocker       0.7333  0.0791
Colley      -0.1173 -0.5261
Variables (the 10 first):
            Can1    Can2
Taille+   0.8511 -1.2317
Taille++ -0.8367 -0.0206
Taille-   1.1850  0.9239
Poids+   -0.3054 -0.8189
Poids++  -1.0151  0.9739
Poids-    1.1689  0.8243
Veloc+    0.6037 -0.8878
Veloc++  -0.8921 -0.3718
Veloc-    0.3199  1.0449
Intell+   0.3694 -0.2855

and factor analysis of mixed data (FAMD).

>>> #FAMD
>>> D = load_heart("subset")
>>> X = D.drop(columns=["disease"])
>>> clf = GFA()
>>> clf.fit(X,y)
GFA()
>>> summaryGFA(clf)
                    General Factor Analysis - Results
Eigenvalues informations:
                         Can1     Can2     Can3     Can4  ...     Can7     Can8     Can9     Can10
Variance               2.3749   1.1618   1.0641   1.0243  ...   0.7950   0.7223   0.4739    0.4363
Difference             1.2131   0.0978   0.0398   0.0441  ...   0.0727   0.2483   0.0376       NaN
% of var.             23.7495  11.6184  10.6408  10.2431  ...   7.9497   7.2227   4.7392    4.3629
Cumulative % of var.  23.7495  35.3678  46.0086  56.2517  ...  83.6751  90.8979  95.6371  100.0000
[4 rows x 10 columns]
Individuals (the 10 first):
     Can1    Can2
0 -1.0697 -0.9664
1  0.8760 -0.8987
2 -0.6487  3.0729
3 -0.8548  1.0046
4  0.2919 -0.3426
5  0.2375 -0.2891
6  2.2459  5.0207
7  2.3149  0.3848
8  0.7471 -0.1014
9  2.8908 -0.2045
Variables (the 10 first):
                   Can1    Can2
age              0.6420  0.3410
restbpress       0.4544  0.2595
max_hrate       -0.7802  0.0723
asympt           1.0310 -0.4093
atyp_angina     -1.0302 -0.0652
non_anginal     -0.8281  1.1019
typ_angina      -1.3976  1.0534
f               -0.1198 -0.1910
t                1.4456  2.3035
left_vent_hyper -1.2533  3.6476
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