R言語を使いWeb教材「アイスクリーム屋さんで学ぶ楽しい統計学」1の「因子分析 (2)」について進めていきます（第9回）。

前回（第8回）と同じ 13種類のアイスクリームの データを用いる。


> getwd()
[1] "C:/Users/Katsumasa/Documents"
> list.files()
[1] "icecream-chosa.csv"
> 多変量データ<-read.csv("icecream-chosa.csv")
> 十三種類の多変量データ<-多変量データ[,c("ミルクティー","マカダミアナッツ","クッキー","チョコレート","アーモンド","ミント","キャラメル","ウォールナッツ","チョコチップ","抹茶","マロン","チョコミント","あずき")
今回は因子得点を求めるので、前回と同様にR関数のfactanal ()を実行するが、引数scoresを加え、"regression" を指定する。
この引数を指定した場合は、バリマックス回転（直交回転）後の因子得点を返す。
> 十三種類因子分析直交<-factanal(十三種類の多変量データ,factors=4,scores="regression")
> print(十三種類因子分析直交$scores,cutoff=0,sort=TRUE)
Factor1 Factor2 Factor3 Factor4
[1,] 1.23616888 -1.62278111 1.030138395 -1.69020883
[2,] 0.82594308 0.04535999 0.736402161 1.26173182
[3,] 0.07694137 -0.65303149 -0.863806538 -1.23957182
[4,] -1.53382056 -0.72976812 0.374828748 0.20508964
[5,] -0.25276794 1.68550851 0.626644255 -0.50478064
[6,] -0.39095243 1.76399630 0.023176757 -0.46939311
[7,] -0.54014768 -1.16741381 1.018614902 -0.77145511
[8,] 0.06177013 0.33973776 -1.666788054 1.28486309
[9,] -1.22345659 0.12868658 0.221976598 1.02683177
[10,] -1.33024621 -0.93968552 0.428942381 -0.83335874
[11,] 0.53459008 -0.44696646 -0.970942390 0.86395795
[12,] -0.66631149 1.55209843 0.647806046 -0.24534821
[13,] 0.37452842 0.44045311 0.720304100 0.87053134
[14,] 1.25733624 -0.27906239 0.782355280 0.56389185
[15,] -0.72019764 1.71340527 0.035047670 -0.17517743
[16,] 0.12159728 0.54259153 -2.257537785 0.10966958
[17,] 0.18917278 -0.15861283 -0.345459734 -0.67644956
[18,] -0.71084634 1.08431675 -0.475415088 -0.22680791
[19,] -0.27867355 0.87059896 0.090410093 0.71062971
[20,] 0.75142777 -0.14852513 -0.425106224 0.88649505
[21,] 0.65540108 -0.77361093 0.889407356 -0.25633914
[22,] 1.21145078 -0.88473758 0.255332244 0.70822020
[23,] 0.18669260 -0.46199603 0.828281772 1.10183787
[24,] 1.38739386 1.74330496 -0.621298581 -1.36385300
[25,] 1.52551752 -1.01771713 0.239435165 1.20621846
[26,] 0.28871791 -1.70623546 1.031785725 -0.35125980
[27,] -0.76862246 -1.74940530 0.420561660 1.44565381
[28,] 0.49064770 0.84674665 0.090086867 -0.04737319
[29,] -0.35823728 0.17647431 0.765056651 1.48351103
[30,] 0.60310648 1.47505111 0.596900122 0.46598033
[31,] 1.24689873 0.53580910 0.138104789 -1.56757698
[32,] 0.75224973 -1.17044430 0.365729529 -0.96214815
[33,] 0.83519654 -0.32081478 0.220370546 0.17000944
[34,] 0.53891217 1.03802462 0.632824928 0.95051983
[35,] -0.35981068 -0.02874733 0.845752926 -0.22856017
[36,] 0.88385545 -0.87848838 -0.310626640 0.13697070
[37,] -1.76150613 -1.63577136 1.149523640 -0.96026943
[38,] 0.40091801 0.29994310 0.160511862 0.23331755
[39,] -0.24441074 1.00165711 -0.499499381 0.12237183
[40,] 1.40177063 -1.31093826 0.298997671 0.61975584
[41,] -0.34905395 0.03876392 -0.361801389 -0.22878659
[42,] -1.50647330 1.29551831 0.754887174 -1.26792450
[43,] 1.14287418 -0.87538421 -3.907880675 -0.52570391
[44,] -0.56953124 0.27935348 -1.567492030 -0.92359953
[45,] 1.71441938 0.39954651 -1.129174729 0.31322285
[46,] 1.56311475 0.51114403 0.132249841 -1.66759262
[47,] -0.27029092 0.23597163 0.812290270 -0.16589544
[48,] -1.00139690 -0.84253178 -0.252950268 0.72322775
[49,] -0.24511138 0.96610275 -1.145132712 1.85152118
[50,] 0.02793254 -1.39521271 -3.806874287 -0.12924546
[51,] -1.12863633 0.81215677 0.211062158 -1.56429059
[52,] -0.02921044 -0.24625527 0.904747265 -1.73474169
[53,] 1.42430054 -1.77800118 0.983948206 -0.11325665
[54,] -0.71172283 -0.19037742 -0.307267167 -0.46329403
[55,] 0.77640030 0.57765027 0.714791542 -0.28138737
[56,] -0.18651968 1.36857616 0.665044244 -0.48861574
[57,] 0.96402384 -1.79595895 0.366647741 0.96704940
[58,] -2.04912565 -1.56229417 0.464761166 0.59460032
[59,] 0.32289348 0.23701527 -0.390447708 -1.18924558
[60,] 0.38631181 0.68217525 -1.664841487 -0.66987013
[61,] -1.50643269 -0.62630968 0.389099512 -0.63279345
[62,] -0.01180284 -0.47512112 0.892047739 -0.38560198
[63,] 0.11555927 0.21081366 0.767948550 0.60631536
[64,] -0.44385369 0.63395343 0.772371152 -0.32741764
[65,] -0.10354333 -0.02296567 -0.980976714 0.07340664
[66,] -0.10901827 -0.05383601 -0.389492107 0.45836111
[67,] -0.41416834 -0.75323415 0.366451734 -1.01854106
[68,] 0.23552091 1.11736121 0.620646740 1.24290661
[69,] -0.37628940 0.03709210 -0.352380116 -0.45922314
[70,] 0.92321537 0.01895842 0.741333059 0.71485285
[71,] 0.52502969 -0.30984299 0.850706102 -0.37980987
[72,] -1.00988391 -0.44944684 -2.101271005 0.71530348
[73,] 0.89644545 1.08016973 0.007687375 0.90991259
[74,] -0.10983042 1.16750664 0.636223184 0.96884981
[75,] -2.28273210 -1.05405866 -0.719275924 -0.74570375
[76,] -0.86228969 -0.39882062 -0.317747990 0.65182405
[77,] 0.03701654 -0.28147458 0.865731997 -0.26211238
[78,] -0.25018242 1.34925247 0.590876298 2.16326502
[79,] -0.38120756 1.36696288 0.082282871 -0.91499783
[80,] -1.84494824 -0.47392937 -1.427660039 -0.27309562
因子負荷量と因子得点を1つの散布図に描くと以下のようになる。
> biplot(十三種類因子分析直交$scores,十三種類因子分析直交$loading)
  1. web教材「アイスクリーム屋さんで学ぶ楽しい統計学」第9章 因子分析 (2) http://kogolab.chillout.jp/elearn/icecream/chap9/sec0.html