大聯盟棒球分析
棒球分析：
棒球指數說明
分析結果
Standard deviations: [1] 2.0375 1.4438 1.0794 0.9677 0.8985 0.7723 0.4137 0.2412 0.1604 0.0612 Rotation: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 H1B -0.42149 0.264429 -0.10485 0.051118 -0.22056 0.07044 -0.53449 0.219239 -0.531244 0.263129 H2B -0.32868 -0.070578 -0.04027 0.299088 0.41349 -0.71589 0.25004 0.017715 -0.201668 0.094504 H3B -0.12465 0.431217 0.26671 0.050748 0.60222 0.51320 0.26555 -0.004658 -0.160131 0.050813 HR -0.05335 -0.601640 -0.16938 -0.391589 0.13189 0.21056 0.15397 -0.290454 -0.527445 0.071796 RBI -0.38556 -0.362363 0.04956 -0.230919 0.11785 0.13797 0.06433 0.723662 0.318688 0.044138 SO 0.33906 -0.283907 0.14094 0.135585 0.53707 -0.02319 -0.68821 0.026480 0.053561 0.002758 BB 0.03395 -0.231905 0.81201 0.219003 -0.28449 -0.02200 0.07999 0.107550 -0.306700 -0.210450 SB 0.01617 0.298275 0.33107 -0.793597 0.09018 -0.38143 -0.12244 -0.046660 -0.002026 -0.023977 AVG -0.48421 -0.006309 -0.07885 0.004151 0.06751 0.05987 -0.20663 -0.247750 0.086449 -0.799732 OBP -0.44321 -0.149935 0.30008 0.050335 -0.06604 0.07545 -0.12538 -0.517420 0.409019 0.477184
分析：
# 指定精準度 options(digits=4) # 清除殘餘變數 rm(list=ls()) Baseball <- read.table('PCA-1.csv', header=T, sep=',') doTask <- function(f) { Baseball.Analysis <- prcomp(f, data=Baseball, center=TRUE, scale=TRUE) # 特徵值 > 1 者即為應選擇因子 print(Baseball.Analysis$sdev^2) plot(Baseball.Analysis, type="line", main="陡坡圖") summary(Baseball.Analysis) Baseball.Analysis } # 使用所有變數分析 q <- doTask(~H1B+H2B+H3B+HR+RBI+SO+BB+SB+AVG+OBP) # 繪製因素負荷量圖 biplot(q, choices=c(1,2), main='因素負荷量圖') biplot(q, choices=c(2,3), main='因素負荷量圖') biplot(q, choices=c(3,1), main='因素負荷量圖')
