1. The document appears to be notes from a workshop on statistical analysis techniques like PCA and MCA. It includes sample R code demonstrating how to conduct PCA and MCA on sample datasets. 2. Results from PCA and MCA are presented on sample datasets, including the eigenvalues and percentage of variance explained by each dimension. MCA was also conducted with the data converted to factors. 3. The notes conclude by comparing the results of PCA and MCA on the sample datasets and discussing some references on related statistical analysis techniques.

1. 2018/03/03
Tokyo.R #68 LT
@419kfj
Kazuo.fujimoto2007@gmail.com

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* 2010 p2-
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3. Q1
• 57
http://www.mhlw.go.jp/bunya/roudoukijun/anzeneisei12/dl/stress-check_j.pdf
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4. ??
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• http://www.mhlw.go.jp/bunya/roudoukijun/anzeneisei12/pdf
/150803-1.pdf
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5. ……
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1 2 3 4
1 2 3 4
1 2 3 4
1 2 =1
………

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7. P105
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MCA
• 2011
• HCPC
• MCA
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•
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p105

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=
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10. FactoMineR PCA( )
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library(FactoMineR)
.d <- matrix(c(2,4,5,
1,5,1,
5,3,4,
2,2,3,
3,5,5,
4,3,2,
4,4,3,
1,2,1,
3,3,2,
5,5,3),byrow=TRUE,10,3,
dimnames = list( =c(" "," "," "," ",
" "," "," "," ",
" "," "),
=c(" "," "," ")))
res.PCA <- PCA(.d)
plot.PCA(res.PCA,choix = "var")
plot.PCA(res.PCA,choix = "ind",col.ind = 1:10)

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13. PCA
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13 1.6234449↑
13 1.510711
12 1.033449
11 0.7504515↑
11 0.6377176
9 -0.2916428
8 -0.6957621
7 -1.0267387↑
7 -1.1082693
4 -2.4333611

14. 5 1
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• Greenacre
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• MCA
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• MCA
PCA
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15. factor
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tribble( ~ ,~ ,~ ,~ ,
#---------------
" ", 2,4,5,
" ", 1,5,1,
" ", 5,3,4,
" ", 2,2,3,
" ", 3,5,5,
" ", 4,3,2,
" ",4,4,3,
" ", 1,2,1,
" ",3,3,2,
" ", 5,5,3 ) %>%
mutate_all(.funs=factor) %>% as.data.frame %>%
column_to_rownames(' ') -> .d.f
# tibble column_to_rownames
Setting row names on a tibble is
deprecated.
data.frame
column_to_rownames

16. MCA
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res.MCA <-MCA(.d.f,graph = FALSE)
plot.MCA(res.MCA,axes = c(1,2),autoLab="yes",col.ind = rep(2,10),
col.var = c(rep(3,5),rep(4,4),rep(5,5)),
title=" - 1:2 ",cex=0.9)

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18. • Dim1
• _5 _1
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• 5,3 4,2
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• 1 5
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• 5,3 4
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19. • PCA
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• CA/MCA
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20. res.MCA
Result summary(res.MCA)
res.MCA
t <- res.MCA$var$coord[1:5,1]
t <- res.MCA$var$coord[6:9,1]
t <- res.MCA$var$coord[10:14,1]
_1 _2 _3 _4 _5
-1.5368811 -0.4624864 0.6398921 0.7685653 0.5909102
_2 _3 _4 _5
-1.20152575 1.23897692 0.02087463 -0.45187617
_1 _2 _3 _4 _5
-1.53688111 1.26353808 -0.15942396 1.18985460 -0.08244833
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21. result
• MCA (factor)
result
_1 _2 _3 _4 _5
-1.5368811 -0.4624864 0.6398921 0.7685653 0.5909102
_2 _3 _4 _5
-1.2015258 1.23897692 0.02087463 -0.4518762
_1 _2 _3 _4 _5
-1.5368811 1.26353808 -0.159424 1.1898546 -0.0824483
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22. .d1 <- data.frame( t[.d.f$ ], t[.d.f $ ], t[.d.f $ ])
rownames(.d1) <- c(" "," "," "," ",
" "," ", " ",
" "," "," ")
.d1
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23. -0.462 0.021 -0.082
-1.537 -0.452 -1.537
0.591 1.239 1.190
-0.462 -1.202 -0.159
0.640 -0.452 -0.082
0.769 1.239 1.264
0.769 0.021 -0.159
-1.537 -1.202 -1.537
0.640 1.239 1.264
0.591 -0.452 -0.159
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25. MCA PCA
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> round(cor(.d),3)
1.000 0.191 0.36
0.191 1.000 0.30
0.360 0.300 1.00
> round(cor(.d2),3)
2 2 2
2 1.00 0.630 0.820
2 0.63 1.000 0.852
2 0.82 0.852 1.000

27. Eigenvalues Dim.1 Dim.2 Dim.3
Variance 1.573 0.814 0.613
% of var. 52.428 27.134 20.438
Cumulative % of
var.
52.428 79.562 100
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Eigenvalues Dim.1 Dim.2 Dim.3
Variance 2.538 0.371 0.091
% of var. 84.597 12.375 3.028
Cumulative % of
var.
84.597 96.972 100
1 5
PCA
MCA
PCA

28. p112 114
Eigenvalues Dim.1 Dim.2 Dim.3
Variance 1.573 0.814 0.613
% of var. 52.428 27.134 20.438
Cumulative % of
var.
52.428 79.562 100
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Eigenvalues Dim.1 Dim.2 Dim.3
Variance 2.538 0.371 0.091
% of var. 84.597 12.375 3.028
Cumulative % of
var.
84.597 96.972 100

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PCA
1 13 1.623 ↑ 2.053
2 13 1.511 1.973
3 12 1.033 1.896
4 11 0.750 ↑ 0.395
5 11 0.638 0.066
6 9 -0.292 -0.013
7 8 -0.696 -0.329
8 7 -1.027 ↑ -1.145
9 7 -1.108 -2.213
10 4 -2.433 -2.684
1-5 PCA PCA

30. ,2011,
Clausen.S.E,1998,”Applied Correspondence Analysis an Intoduction”,SAGE Publication,
( ,2015, )
Greenacre.M.J,2017,”Correspondence Analysis in Practice 3rd Edition”,CRC
Hand, David J,2016,“Measurement”, very short introduction ,oxford university press
Francois Husson, Sebastien Le, Jérôme Pagès,2017,” Exploratory Multivariate Analysis
by Example Using R, Second Edition”, Chapman and Hall/CRC
,2004(1981), 3
p293−294,
,2007, ― ,
,2010, ―
,
,2014,
, 41 2
81
URL=https://www.jstage.jst.go.jp/article/jbhmk/41/2/41_89/_pdf/-char/ja
,2006,
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