20190911
次元縮約＋クラスター分析：東京の自殺を例に
ライブラリーの読み込み
> library(readr)
> library(dplyr)
> library(clustrd)
> library(knitr)データの読み込みと表示
> mori1 <- read_csv("mori1.csv")
> sui1 <- select(mori1, code, rate, industry1, aging:taxgain)
> sui1.tokyo <- sui1[which(as.numeric(sui1$code) %/% 1000 == 13),]
> kable(sui1.tokyo[1:10,])| code | rate | industry1 | aging | unemployment | taxgain |
|---|---|---|---|---|---|
| 13101 | 19.59 | 0.0003645 | 0.1761120 | 0.0181717 | 0.1134598 |
| 13102 | 19.77 | 0.0003888 | 0.1607417 | 0.0240528 | 0.1948065 |
| 13103 | 17.35 | 0.0006927 | 0.1754911 | 0.0273618 | 0.5428776 |
| 13104 | 20.16 | 0.0006748 | 0.1956889 | 0.0343030 | 0.3382430 |
| 13105 | 11.96 | 0.0006616 | 0.1909031 | 0.0272838 | 0.2612840 |
| 13106 | 18.35 | 0.0006166 | 0.2352163 | 0.0376425 | 0.1590275 |
| 13107 | 17.11 | 0.0007521 | 0.2270851 | 0.0371070 | 0.1969722 |
| 13108 | 16.95 | 0.0006992 | 0.2108695 | 0.0351113 | 0.4161563 |
| 13109 | 10.32 | 0.0009192 | 0.2022644 | 0.0329637 | 0.3759405 |
| 13110 | 11.20 | 0.0017310 | 0.1988243 | 0.0311272 | 0.3609426 |
Tandem(varimax再掲)
> sui1.outTandem.1 <- cluspca(sui1.tokyo[,-1], 3, 2,
+ alpha = 0.3,
+ rotation = "varimax",
+ scale=TRUE,nstart = 10, center = TRUE,seed = 1234)
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|=================================================================| 100%> summary(sui1.outTandem.1)Solution with 3 clusters of sizes 39 (62.9%), 14 (22.6%), 9 (14.5%) in 2 dimensions. Variables were mean centered and standardized.
Cluster centroids:
Dim.1 Dim.2
Cluster 1 0.6141 1.5246
Cluster 2 0.4016 -0.3240
Cluster 3 0.4016 -0.3240
Variable scores:
Dim.1 Dim.2
rate 0.0687 0.0033
industry1 -0.5536 -0.0628
aging -0.7811 0.0093
unemployment 0.2804 -0.0602
taxgain -0.0109 0.9962
Within cluster sum of squares by cluster:
[1] 11.4352 10.5876 3.2486
(between_SS / total_SS = 34.01 %)
Clustering vector:
[1] 1 1 2 2 1 1 1 2 2 2 2 2 2 1 2 1 1 1 2 2 2 1 2 2 1 1 1 1 1 1 1 1 1 1 1
[36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 1 3 1 1
Objective criterion value: 64.996
Available output:
[1] "obscoord" "attcoord" "centroid" "cluster" "criterion"
[6] "size" "odata" "scale" "center" "nstart" > plot(sui1.outTandem.1, cludesc = TRUE)
Tandem(none)
> sui1.outTandem.2 <- cluspca(sui1.tokyo[,-1], 3, 2,
+ alpha = 0.3,
+ rotation = "none",
+ scale=TRUE,nstart = 10, center = TRUE,seed = 1234)
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|=================================================================| 100%> summary(sui1.outTandem.2)Solution with 3 clusters of sizes 39 (62.9%), 14 (22.6%), 9 (14.5%) in 2 dimensions. Variables were mean centered and standardized.
Cluster centroids:
Dim.1 Dim.2
Cluster 1 0.2222 -0.4657
Cluster 2 1.2128 1.1094
Cluster 3 -2.8493 0.2924
Variable scores:
Dim.1 Dim.2
rate 0.0634 -0.0267
industry1 -0.5264 0.1826
aging -0.7003 0.3460
unemployment 0.2268 -0.1754
taxgain 0.4207 0.9030
Within cluster sum of squares by cluster:
[1] 11.4352 10.5876 3.2486
(between_SS / total_SS = 82.85 %)
Clustering vector:
[1] 1 1 2 2 1 1 1 2 2 2 2 2 2 1 2 1 1 1 2 2 2 1 2 2 1 1 1 1 1 1 1 1 1 1 1
[36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 1 3 1 1
Objective criterion value: 64.996
Available output:
[1] "obscoord" "attcoord" "centroid" "cluster" "criterion"
[6] "size" "odata" "scale" "center" "nstart" > plot(sui1.outTandem.2, cludesc = TRUE)