This document discusses geometric data analysis (GDA) techniques including correspondence analysis (CA), multiple correspondence analysis (MCA), and generalized discriminant analysis (GDA). It provides an overview of these methods, how they are related, and examples of their applications. Key references on CA, MCA, and GDA are also cited for further reading.

1. < >
CA/MCA GDA
92
13 < > < >

2. • V1.0 2019/10/06
• 1.2 2019/10/09 Speaker Deck

3. •
• (CA)
• CA MCA
• GDA
•
•

4. • …
• P. T.

5. ….
• (Correspondence Analysis:CA)
(Multiple Correspondence
Analysis:MCA)
•
MCA

6. • CA/MCA
• GDA

7. • CA/MCA
…
• GDA
•
–
–
• R/RStudio

8. •
– 2
• CA
–
– mass /
–
2 3
•

9. Low High
301 497 208 50 254 187
261 550 250 27 339 157
/ 361 534 204 59 324 216
/ 463 766 334 72 350 601
89 350 195 12 143 167
23 182 124 10 60 110
117 298 145 11 184 56
104 379 219 21 152 213
130 352 153 17 272 264
168 370 187 51 162 424
Clausen1998=2015 4

10. •
–
•
–
•
– Χ2
• …

12. • /
–
• Contribution
–
• COS2
– 2

13. •
– summary(res.CA)
•
– Factoshiny res.CA
– explor res.CA
•

14. •
–
–
–

15. • / /
• →
•
• …
–

16. •
•
•
Greenacre 2017:90-93

17. CA MCA

18. • 2
•
•

19. MCA
• CA
•
– 1/0
indicator CA
– 2 Burt
CA

20. MCA
•
– Greenacre
• Adjusted
• jointCA
• MCA
– Le Roux
• Specific MCA
• Class Specific MCA

21. TV Film Art Eat Gender Age Income
Tv-Comedy:
164
Action :405
Impressionism :
126
Fish&Chips :
113
Men :534
18-24:
102
<£9 :232
Tv-Drama :
135
Comedy :247 Landscape :645
FrenchRest :
99
Women:
719
25-34:
259
£10-19:
261
Tv-Films :
122
CostumeDrama:
141
ModernArt :117
IndianRest :
419
35-44:
269
£20-29:
206
Tv-Nature:
162
Documentary :
100
PerformanceArt:
110
ItalianRest:
240
45-54:
196
£30-39:
127
Tv-News :
228
Horror :
64
Portrait :123 Pub :284
55-64:
184
£40-59:
131
Tv-Police:
85
Musical : 87
RenaissanceArt:
59
SteakHouse :
98
65+ :
243
>=£60 :
130
Tv-Soap :
216
Romance :104 StillLife : 73 ? :166
Tv-Sport :
141
SciFi :105
Taste

22. •

23. GDA
• MCA
•
–
Patrick
Suppes Stanford University , 1996

24. •
– /
•
ANOVA
–
•
• ANOVA
→ GDA

25. MCA
• Active
–
•
•
–
–

29. •
–
•
•
•
η2

30. • V total
• V within
• V between
V total = V within + V between
η2 = V between / V total
η2 p113
12

31. F
• / ← ( )
• / ←
• F = /
within
between

32. η2
A MA VA
within
B MB VB
within
A B
VA VB
A B VAB between
η2 = Vbetween / Vtotal( )
BA
VA
VB
VAB
VAB
between
η2
η2

33. •
• = x

35. SDA IDA
PCA,CA/MCA
GDA
GDA
Rouanet,Le Roux 2010 1
GDA

37. • GDA
– Le Roux, B. & Rouanet, H. 2010 , Multiple Correspondence Analysis, SAGE
publish,
– Bennet. T et al, 2009, “Culture, Class, Distinction” , Rontledge ,
2017
• GDA
– Le Roux, B. & Rouanet, H. 2010 , Multiple Correspondence Analysis, SAGE
publish,
– Le Roux, B. & Rouanet, H. 2004 , “Geometric Data Analysis: From
Correspondence Analysis to Structured Data.”, Dordrecht: Kluwer.
•
– Clausen, Sten-Erik 1998 Applied Correspondence Analysis, Sage publication
, 2015,
•
– Greenacre M, 2017, Correspondence Analysis in Practice Third Edition, CRC
Press. , 2019 ― ―
2019

38. speaker deck
fujimoto@sakushin-u.ac.jp