discrimintools.summaryGFALDA#

discrimintools.summaryGFALDA(obj, digits=4, detailed=False, to_markdown=False, tablefmt='github', **kwargs)[source]#

Printing summaries of General Factor Analysis Linear Discriminant Analysis model.

Parameters:

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

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

  • detailed (bool, default = False) – To print detailed summaries.

  • 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

summaryCANDISC

Printing summaries of Canonical Discriminant Analysis model.

summaryCPLS

Printing summaries of Partial Least Squares for Classification model.

summaryDA

Printing summaries of Discriminant Analysis model.

summaryDiCA

Printing summaries of Discriminant Correspondence Analysis model.

summaryDISCRIM

Printing summaries of Discriminant Analysis (linear and quadratic) model.

summaryMDA

Printing summaries of Mixed Discriminant Analysis model.

summaryPLSDA

Printing summaries of Partial Least Squares Discriminant Analysis model.

summaryPLSLDA

Printing summaries of Partial Least Squares Linear Discriminant Analysis model.

summarySTEPDISC

Printing summaries of Stepwise Discriminant Analysis model.

Examples

>>> from discrimintools.datasets import load_alcools, load_vote, load_heart
>>> from discrimintools import GFALDA, summaryGFALDA

The GFALDA class performs principal component analysis - discriminant analysis (PCADA)…

>>> #PCA + LDA = PCADA
>>> D = load_alcools("train")
>>> y, X = D["TYPE"], D.drop(columns=["TYPE"])
>>> clf = GFALDA()
>>> clf.fit(X,y)
GFALDA()
>>> summaryGFALDA(clf)
                    General Factor Analysis Linear Discriminant Analysis - Results
Class Level Information:
        Frequency  Proportion  Prior Probability
KIRSCH         17      0.3269             0.3269
MIRAB          15      0.2885             0.2885
POIRE          20      0.3846             0.3846
Importance of components:
      Eigenvalue  Difference  Proportion (%)  Cumulative (%)
Can1      2.7988      1.0799         34.9848         34.9848
Can2      1.7188      0.3154         21.4856         56.4703
Raw Canonical Coefficients:
            Can1    Can2
Constant -3.9272 -2.1923
MEOH      0.0014  0.0001
ACET      0.0005  0.0029
BU1       0.0418 -0.0025
BU2      -0.0005  0.0107
ISOP      0.0096  0.0002
MEPR      0.0264  0.0048
PRO1     -0.0003  0.0010
ACAL      0.0230  0.0432
Projection functions coefficients:
        Can1    Can2
MEOH  0.0651  0.0057
ACET  0.0076  0.0434
BU1   0.0570 -0.0034
BU2  -0.0037  0.0721
ISOP  0.0575  0.0012
MEPR  0.0604  0.0110
PRO1 -0.0244  0.0807
ACAL  0.0230  0.0431

discriminant analysis on qualitative variables (DISQUAL)…

>>> #MCA + LDA = DISQUAL
>>> D = load_vote("train")
>>> y, X = D["group"], D.drop(columns=["group"])
>>> clf = GFALDA(n_components=5)
>>> clf.fit(X,y)
GFALDA(n_components=5)
>>> summaryGFALDA(clf)
                    General Factor Analysis Linear Discriminant Analysis - Results
Class Level Information:
            Frequency  Proportion  Prior Probability
democrat          154      0.6553             0.6553
republican         81      0.3447             0.3447
Importance of components:
      Eigenvalue  Difference  Proportion (%)  Cumulative (%)
Can1      0.4912      0.2018         24.5584         24.5584
Can2      0.2893      0.1926         14.4672         39.0255
Can3      0.0968      0.0023          4.8380         43.8635
Can4      0.0945      0.0095          4.7226         48.5861
Can5      0.0850      0.0065          4.2499         52.8360
Raw Canonical Coefficients:
                                    Can1      Can2      Can3      Can4      Can5
Constant                         -3.6423  -71.5330   -0.5466   -0.3136   -8.7288
handicapped_infants_n             1.4104   -0.0444   -1.3628   -1.3368   -0.9953
handicapped_infants_other        -7.7080  112.3077  228.0942 -137.3374  118.7663
handicapped_infants_y            -1.8593   -0.5920    0.5157    2.6020    0.6542
water_project_cost_sharin_n      -0.3863   -0.8587   -3.4385   -3.4936    0.8114
...
duty_free_exports_other           6.4025   76.2179  -70.0049  -26.0326  -51.8400
duty_free_exports_y              -2.6233   -0.4362   -0.3366   -0.4600   -0.1570
export_administration_act_n      14.1955   -2.4783    9.4113   10.6011   -8.8429
export_administration_act_other  -2.4658    3.0700   -2.1882    6.4082    2.9454
export_administration_act_y      -0.1613   -0.4680   -0.0029   -1.6712   -0.1645
Projection functions coefficients:
                                   Can1    Can2    Can3    Can4    Can5
handicapped_infants_n            0.0458 -0.0014 -0.0442 -0.0434 -0.0323
handicapped_infants_other       -0.0164  0.2390  0.4853 -0.2922  0.2527
handicapped_infants_y           -0.0519 -0.0165  0.0144  0.0727  0.0183
water_project_cost_sharin_n     -0.0106 -0.0235 -0.0942 -0.0957  0.0222
water_project_cost_sharin_other -0.0006  0.1209  0.1482 -0.1715  0.0665
...
duty_free_exports_other          0.0204  0.2432 -0.2234 -0.0831 -0.1654
duty_free_exports_y             -0.0712 -0.0118 -0.0091 -0.0125 -0.0043
export_administration_act_n      0.1133 -0.0198  0.0751  0.0846 -0.0706
export_administration_act_other -0.0407  0.0506 -0.0361  0.1057  0.0486
export_administration_act_y     -0.0061 -0.0178 -0.0001 -0.0636 -0.0063

and discriminant analysis on mixed predictors (DISMIX).

>>> #FAMD + LDA = DISMIX
>>> D = load_heart("subset")
>>> y, X = D["disease"], D.drop(columns=["disease"])
>>> clf = GFALDA(n_components=5)
>>> clf.fit(X,y)
GFALDA(n_components=5)
>>> summaryGFALDA(clf)
                    General Factor Analysis Linear Discriminant Analysis - Results
Class Level Information:
          Frequency  Proportion  Prior Probability
negative        117      0.5598             0.5598
positive         92      0.4402             0.4402
Importance of components:
      Eigenvalue  Difference  Proportion (%)  Cumulative (%)
Can1      2.3749      1.2131         23.7495         23.7495
Can2      1.1618      0.0978         11.6184         35.3678
Can3      1.0641      0.0398         10.6408         46.0086
Can4      1.0243      0.0441         10.2431         56.2517
Can5      0.9802      0.0131          9.8024         66.0541
Raw Canonical Coefficients:
                         Can1    Can2    Can3    Can4    Can5
Constant              -1.8294 -4.1264 -2.2226 -3.4631 -3.2657
age                    0.0518  0.0394 -0.0015  0.0005  0.0217
restbpress             0.0170  0.0138  0.0133  0.0246  0.0163
max_hrate             -0.0213  0.0028  0.0038  0.0011  0.0003
asympt                 0.4341 -0.3523 -0.1561 -0.3150 -0.2125
...
left_vent_hyper       -0.5277  3.1395 -2.1900  0.6887 -3.1521
normal                -0.0688 -0.0481 -0.0640 -0.2254  0.0214
st_t_wave_abnormality  0.4870 -0.2444  0.7363  1.1923  0.4011
no                    -0.3450  0.1151  0.0791  0.0815  0.1269
yes                    0.6565 -0.2189 -0.1505 -0.1550 -0.2415
Projection functions coefficients:
                         Can1    Can2    Can3    Can4    Can5
age                    0.1389  0.1054 -0.0040  0.0012  0.0581
restbpress             0.0983  0.0803  0.0771  0.1428  0.0944
max_hrate             -0.1687  0.0224  0.0298  0.0086  0.0028
asympt                 0.1672 -0.0949 -0.0403 -0.0797 -0.0526
atyp_angina           -0.1671 -0.0151  0.1161  0.2078 -0.1317
...
left_vent_hyper       -0.2033  0.8460 -0.5648  0.1743 -0.7802
normal                -0.0265 -0.0130 -0.0165 -0.0570  0.0053
st_t_wave_abnormality  0.1876 -0.0659  0.1899  0.3017  0.0993
no                    -0.1329  0.0310  0.0204  0.0206  0.0314
yes                    0.2529 -0.0590 -0.0388 -0.0392 -0.0598
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