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The SPLUS program for chapter 16.
Table 16.1, p. 384.
First 15 observations only.
print( companies[ 1:15, ]) type types symbol obsno ror5 de salesgr5 eps5 npm1 pe payoutr1 1 1 Chem dia 1 13.0 0.7 20.2 15.5 7.2 9 0.4263980 2 1 Chem dow 2 13.0 0.7 17.2 12.7 7.3 8 0.3806930 3 1 Chem stf 3 13.0 0.4 14.5 15.1 7.9 8 0.4067800 4 1 Chem dd 4 12.2 0.2 12.9 11.1 5.4 9 0.5681820 5 1 Chem uk 5 10.0 0.4 13.6 8.0 6.7 5 0.3245440 6 1 Chem psm 6 9.8 0.5 12.1 14.5 3.8 6 0.5108083 7 1 Chem gra 7 9.9 0.5 10.2 7.0 4.8 10 0.3789130 8 1 Chem hpc 8 10.3 0.3 11.4 8.7 4.5 9 0.4819280 9 1 Chem mtc 9 9.5 0.4 13.5 5.9 3.5 11 0.5732480 10 1 Chem acy 10 9.9 0.4 12.1 4.2 4.6 9 0.4907980 11 1 Chem cz 11 7.9 0.4 10.8 16.0 3.4 7 0.4891300 12 1 Chem ald 12 7.3 0.6 15.4 4.9 5.1 7 0.2722770 13 1 Chem rom 13 7.8 0.4 11.0 3.0 5.6 7 0.3156460 14 1 Chem rei 14 6.5 0.4 18.7 -3.1 1.3 10 0.3840000 15 2 Heal hum 15 9.2 2.7 39.8 34.4 5.8 21 0.3908790
Creating the data set companies.std containing the standardized variables.
companies.std <- companies attach(companies.std) companies.std$pe.std <- ( pe-mean(pe) ) / stdev(pe) companies.std$ror5.std <- ( ror5 -mean(ror5 ) ) / stdev(ror5 ) companies.std$de.std <- ( de -mean(de ) ) / stdev(de ) companies.std$salesgr5.std <- ( salesgr5 -mean(salesgr5 ) ) / stdev(salesgr5 ) companies.std$eps5.std <- ( eps5 -mean(eps5 ) ) / stdev(eps5 ) companies.std$npm1.std <- ( npm1 -mean(npm1 ) ) / stdev(npm1 ) companies.std$payoutr1.std <- ( payoutr1 -mean(payoutr1 ) ) / stdev(payoutr1 ) detach(companies.std)
Table 16.2, p. 386.
First 15 observations only.
print( companies.std[1:15, cbind( 1, 2, 3, 13, 14, 15, 16, 17, 12, 11)])
type types symbol ror5.std de.std salesgr5.std eps5.std
1 1 Chem dia 0.96293197 -0.007359609 0.42906692 0.27749331
2 1 Chem dow 0.96293197 -0.007359609 0.05047846 -0.05683598
3 1 Chem stf 0.96293197 -0.559330289 -0.29025115 0.22973198
4 1 Chem dd 0.66059854 -0.927310743 -0.49216499 -0.24788129
5 1 Chem uk -0.17081839 -0.559330289 -0.40382769 -0.61803158
6 1 Chem psm -0.24640174 -0.375340062 -0.59312191 0.15808999
7 1 Chem gra -0.20861007 -0.375340062 -0.83289460 -0.73743489
8 1 Chem hpc -0.05744335 -0.743320516 -0.68145922 -0.53444925
9 1 Chem mtc -0.35977678 -0.559330289 -0.41644730 -0.86877855
10 1 Chem acy -0.20861007 -0.559330289 -0.59312191 -1.07176419
11 1 Chem cz -0.96444364 -0.559330289 -0.75717691 0.33719497
12 1 Chem ald -1.19119371 -0.191349836 -0.17667461 -0.98818186
13 1 Chem rom -1.00223532 -0.559330289 -0.73193768 -1.21504817
14 1 Chem rei -1.49352714 -0.559330289 0.23977269 -1.94340841
15 2 Heal hum -0.47315182 3.672444925 2.90251150 2.53421603
npm1.std pe.std payoutr1
1 1.28929042 -0.23732851 0.4263980
2 1.33381012 -0.44192205 0.3806930
3 1.60092830 -0.44192205 0.4067800
4 0.48793588 -0.23732851 0.5681820
5 1.06669194 -1.05570269 0.3245440
6 -0.22437927 -0.85110914 0.5108083
7 0.22081770 -0.03273497 0.3789130
8 0.08725861 -0.23732851 0.4819280
9 -0.35793836 0.17185858 0.5732480
10 0.13177830 -0.23732851 0.4907980
11 -0.40245806 -0.64651560 0.4891300
12 0.35437679 -0.64651560 0.2722770
13 0.57697527 -0.64651560 0.3156460
14 -1.33737169 -0.03273497 0.3840000
15 0.66601467 2.21779402 0.3908790
Print out of the first observation to be plotted in the profile diagram in fig. 16.3.
print(companies.std[1, cbind( 13, 14, 15, 16, 17, 12, 18) ] ) ror5.std de.std salesgr5.std eps5.std npm1.std pe.std 1 0.962932 -0.007359609 0.4290669 0.2774933 1.28929 -0.2373285 payoutr1.std 1 0.1505715
Creating the transposed data in order to generate the profile diagram.
var1 <- rbind( 1, 2, 3, 4, 5, 6, 7)
values1 <- rbind( 0.962932, -0.007359609, 0.4290669,
0.2774933, 1.28929, -0.2373285, 0.1505715)
graph <- data.frame(values, var1)
Fig. 16.3, p. 387
Profile diagram of first observation in standardized data set.
plot(graph$var1, graph$values, ylim=c(-3, 2.5), type="b",
ylab="Standardized Values", xlab="Variables")
abline(h=0)
legend(1, -0.5, c("1=ror5", "2=de", "3=salesgr5", "4=eps5", "5=npm1",
"6=pe", "7=payoutr1"))
Print out of the values for observations 16-21 to add to the graph data set.
print(companies.std[15:21, cbind(13, 14, 15, 16, 17, 12, 18)] )
ror5.std de.std salesgr5.std eps5.std npm1.std pe.std
15 -0.4731518 3.672444925 2.9025115 2.5342160 0.6660147 2.2177940
16 -0.5865269 0.360620844 1.3881577 1.2327199 1.0666919 2.4223876
17 -0.7754852 0.912591524 2.7636957 1.3640635 0.2653374 1.8086069
18 -0.5487352 0.728601298 0.6688396 1.0416746 0.7550541 1.8086069
19 0.9251403 -0.743320516 -0.1009569 0.3610756 0.6214950 0.7856392
20 1.7943489 -0.007359609 -0.1892942 -0.1881796 -1.2483323 -0.4419221
21 3.0036826 -0.927310743 -0.2271531 -0.1881796 -1.2038126 -0.2373285
payoutr1.std
15 -0.1347352
16 -1.9789110
17 -0.8403831
18 -0.8380698
19 -0.9651280
20 1.5364365
21 1.3708303
Adding the values for observations 15-21 to the graph data set.
graph$values15 <- rbind(0.4731518, 3.672444925, 2.9025115, 2.5342160, 0.6660147, 2.2177940, 0.1347352) graph$values16 <- rbind(0.5865269, 0.360620844, 1.3881577, 1.2327199, 1.0666919, 2.4223876, 1.9789110) graph$values17 <- rbind(0.7754852, 0.912591524, 2.7636957, 1.3640635, 0.2653374, 1.8086069, 0.8403831) graph$values18 <- rbind(0.5487352, 0.728601298, 0.6688396, 1.0416746, 0.7550541, 1.8086069, 0.8380698) graph$values19 <- rbind(-0.9251403, -0.743320516, -0.1009569, 0.3610756, 0.6214950, 0.7856392, 0.8380698) graph$values20 <- rbind(-1.7943489, -0.007359609, -0.1892942, -0.1881796, -1.2483323, -0.4419221, -1.5364365) graph$values21 <- rbind(-3.0036826, -0.927310743, -0.2271531, -0.1881796, -1.2038126, -0.2373285, -1.3708303)
Fig. 16.4, p. 388.
plot(graph$var1, graph$values15, ylim=c(-4, 4), type="b",
ylab="Standardized Values", xlab="Variables", pch=2)
abline(h=0)
legend(2.5, -1.5, c("1= -1*ror5", "2=de", "3=salesgr5", "4=eps5", "5=npm1",
"6=pe", "7= -1*payoutr1"))
points(graph$var1, graph$values16, type="b", pch=6)
points(graph$var1, graph$values17, type="b", pch=18)
points(graph$var1, graph$values18, type="b", pch=0)
points(graph$var1, graph$values19, type="b", pch=15)
points(graph$var1, graph$values20, type="b", pch=5, lty=4)
points(graph$var1, graph$values21, type="b", pch=17, lty=4)
legend(5.2, -1.5, c("obs 15", "obs 16", "obs 17", "obs 18", "obs 19", "obs 20", "obs 21"),
marks=c(2, 6, 18, 0, 15, 5, 17))
Creating the subset for use in the function agnes.
comp.subset <- companies.std[, cbind(12, 13, 14, 15, 16, 11, 17)]
Fig. 16.9, p. 399.
Not an exact match to the figure in the book but very close.
plot(agnes(comp.subset),which=2)
Creating the subset to be used in the function pam.
attach(companies.std) comp.subset <- cbind(ror5, de, salesgr5, eps5, npm1, pe, payoutr1) detach(companies.std)
Table 16.4, p. 401.
These results do not exactly match any of those displayed in the book.
Three clusters.
pam3 <- pam( comp.subset, 3) print(pam3$clustering) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 1 1 1 1 2 1 2 2 2 2 1 2 2 2 3 3 3 3 1 1 1 1 1 1 2
Table 16.4, p. 401.
Four clusters.
pam4 <- pam( comp.subset, 4) print(pam4$clustering) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 1 1 1 1 2 3 2 2 2 2 3 2 2 2 4 4 4 4 1 1 1 3 3 3 2
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