Source code for discrimintools.summary._summaryda
# -*- coding: utf-8 -*-
#interns functions
from ._summarycandisc import summaryCANDISC
from ._summarycpls import summaryCPLS
from ._summarydica import summaryDiCA
from ._summarydiscrim import summaryDISCRIM
from ._summarygfalda import summaryGFALDA
from ._summarymda import summaryMDA
from ._summaryplsda import summaryPLSDA
from ._summaryplslda import summaryPLSLDA
[docs]
def summaryDA(
obj,**kwargs
):
"""
Printing summaries of Discriminant Analysis model.
Parameters
----------
obj : `class <https://docs.python.org/3/tutorial/classes.html>`_
An object of class :class:`~discrimintools.CANDISC`, :class:`~discrimintools.CPLS`, :class:`~discrimintools.DiCA`, :class:`~discrimintools.DISCRIM`, :class:`~discrimintools.GFALDA`, :class:`~discrimintools.MDA`, :class:`~discrimintools.PLSDA` or :class:`~discrimintools.PLSLDA`.
**kwargs:
Additionals parameters to pass to summary.
Returns
-------
NoneType
See also
--------
:class:`~discrimintools.summaryCANDISC`
Printing summaries of Canonical Discriminant Analysis model.
:class:`~discrimintools.summaryCPLS`
Printing summaries of Partial Least Squares for Classification model.
:class:`~discrimintools.summaryDiCA`
Printing summaries of Discriminant Correspondence Analysis model.
:class:`~discrimintools.summaryDISCRIM`
Printing summaries of Discriminant Analysis (linear and quadratic) model.
:class:`~discrimintools.summaryGFALDA`
Printing summaries of General Factor Analysis Linear Discriminant Analysis model.
:class:`~discrimintools.summaryMDA`
Printing summaries of Mixed Discriminant Analysis model.
:class:`~discrimintools.summaryPLSDA`
Printing summaries of Partial Least Squares Discriminant Analysis model.
:class:`~discrimintools.summaryPLSLDA`
Printing summaries of Partial Least Squares Linear Discriminant Analysis model.
:class:`~discrimintools.summarySTEPDISC`
Printing summaries of Stepwise Discriminant Analysis model.
Examples
--------
>>> from discrimintools.datasets import load_heart
>>> from discrimintools import DISCRIM, summaryDA
>>> D = load_heart("train") # load training data
>>> y, X = D["disease"], D.drop(columns=["disease"]) # split into X and y
>>> clf = DISCRIM()
>>> clf.fit(X,y)
Categorical features have been encoded into binary variables.
DISCRIM(priors='prop')
>>> summaryDA(clf)
Discriminant Analysis - Results
Summary Information:
Infos Value DF DF value
0 Total Sample Size 150 DF Total 149
1 Variables 18 DF Within Classes 148
2 Classes 2 DF Between Classes 1
Class Level Information:
Frequency Proportion Prior Probability
absence 82 0.5467 0.5467
presence 68 0.4533 0.4533
Linear Discriminant Function for disease:
absence presence
Constant -124.3546 -127.3863
age 1.1836 1.1914
sexmale 14.2659 15.9123
chestpainatypicalAngina 0.5668 -1.8839
chestpainnonAnginal 3.4872 1.6652
chestpaintypicalAngina -2.8081 -7.4223
restbpress 0.3460 0.3690
cholesteral 0.0407 0.0373
sugarlow 10.4057 11.7146
electrosttAbnormality -15.8118 -12.8213
electroventricHypertrophy -1.8553 -1.2411
maxHeartRate 0.4856 0.4579
ExerciseAnginayes 4.9170 5.9612
oldpeak 3.0652 3.7751
slopeflat 18.6429 19.8215
slopeupsloping 14.7467 15.2526
vesselsColored -2.5087 -1.0308
thalnormal 21.1525 20.1211
thalreversableEffect 14.8540 16.1636
"""
if obj.model_ == "candisc":
summaryCANDISC(obj=obj,**kwargs)
elif obj.model_ == "cpls":
summaryCPLS(obj=obj,**kwargs)
elif obj.model_ == "dica":
summaryDiCA(obj=obj,**kwargs)
elif obj.model_ == "discrim":
summaryDISCRIM(obj=obj,**kwargs)
elif obj.model_ == "gfalda":
summaryGFALDA(obj=obj,**kwargs)
elif obj.model_ == "mda":
summaryMDA(obj=obj,**kwargs)
elif obj.model_ == "plsda":
summaryPLSDA(obj=obj,**kwargs)
elif obj.model_ == "plslda":
summaryPLSLDA(obj=obj,**kwargs)
else:
raise ValueError("'obj' must be an object of class CANDISC, CPLS, DiCA, DISCRIM, GFALDA, MDA, PLSDA or PLSLDA")
