Exercise 1
# 讀取package MASS
library(MASS)
ls("package:MASS")
## [1] "abbey" "accdeaths" "addterm"
## [4] "Aids2" "Animals" "anorexia"
## [7] "area" "as.fractions" "bacteria"
## [10] "bandwidth.nrd" "bcv" "beav1"
## [13] "beav2" "biopsy" "birthwt"
## [16] "Boston" "boxcox" "cabbages"
## [19] "caith" "Cars93" "cats"
## [22] "cement" "chem" "con2tr"
## [25] "contr.sdif" "coop" "corresp"
## [28] "cov.mcd" "cov.mve" "cov.rob"
## [31] "cov.trob" "cpus" "crabs"
## [34] "Cushings" "DDT" "deaths"
## [37] "denumerate" "dose.p" "drivers"
## [40] "dropterm" "eagles" "enlist"
## [43] "epil" "eqscplot" "farms"
## [46] "fbeta" "fgl" "fitdistr"
## [49] "forbes" "fractions" "frequency.polygon"
## [52] "GAGurine" "galaxies" "gamma.dispersion"
## [55] "gamma.shape" "gehan" "genotype"
## [58] "geyser" "gilgais" "ginv"
## [61] "glm.convert" "glm.nb" "glmmPQL"
## [64] "hills" "hist.FD" "hist.scott"
## [67] "housing" "huber" "hubers"
## [70] "immer" "Insurance" "is.fractions"
## [73] "isoMDS" "kde2d" "lda"
## [76] "ldahist" "leuk" "lm.gls"
## [79] "lm.ridge" "lmsreg" "lmwork"
## [82] "loglm" "loglm1" "logtrans"
## [85] "lqs" "lqs.formula" "ltsreg"
## [88] "mammals" "mca" "mcycle"
## [91] "Melanoma" "menarche" "michelson"
## [94] "minn38" "motors" "muscle"
## [97] "mvrnorm" "nclass.freq" "neg.bin"
## [100] "negative.binomial" "negexp.SSival" "newcomb"
## [103] "nlschools" "npk" "npr1"
## [106] "Null" "oats" "OME"
## [109] "painters" "parcoord" "petrol"
## [112] "phones" "Pima.te" "Pima.tr"
## [115] "Pima.tr2" "polr" "psi.bisquare"
## [118] "psi.hampel" "psi.huber" "qda"
## [121] "quine" "Rabbit" "rational"
## [124] "renumerate" "rlm" "rms.curv"
## [127] "rnegbin" "road" "rotifer"
## [130] "Rubber" "sammon" "select"
## [133] "Shepard" "ships" "shoes"
## [136] "shrimp" "shuttle" "Sitka"
## [139] "Sitka89" "Skye" "snails"
## [142] "SP500" "stdres" "steam"
## [145] "stepAIC" "stormer" "studres"
## [148] "survey" "synth.te" "synth.tr"
## [151] "theta.md" "theta.ml" "theta.mm"
## [154] "topo" "Traffic" "truehist"
## [157] "ucv" "UScereal" "UScrime"
## [160] "VA" "waders" "whiteside"
## [163] "width.SJ" "write.matrix" "wtloss"
# 發現檔案有165個項目，接著列出94~103項
ls("package:MASS")[94:103]
## [1] "minn38" "motors" "muscle"
## [4] "mvrnorm" "nclass.freq" "neg.bin"
## [7] "negative.binomial" "negexp.SSival" "newcomb"
## [10] "nlschools"
Exercise 2
# 讀入檔案 women
dta<-women
# 計算中心化高度
dta$height_centered <- with(dta,(height-mean(height)))
# 計算係數
coef(lm(weight ~ height_centered, data = dta))
## (Intercept) height_centered
## 136.7333 3.4500
#由於中心化只會產生中心水平移動的現象，並不會影響變項之關聯與產生斜率之改變
Exercise 3
# 此處將women當中的height與weight視為兩個不同之date frame進行合併
c(women)
## $height
## [1] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
##
## $weight
## [1] 115 117 120 123 126 129 132 135 139 142 146 150 154 159 164
# 此處將women當中的height與weight以矩陣形式進行合併
c(as.matrix(women))
## [1] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 115 117
## [18] 120 123 126 129 132 135 139 142 146 150 154 159 164
Exercise 4
# 先對於檔案birthwt{MASS}進行初步檢視
head(birthwt)
## low age lwt race smoke ptl ht ui ftv bwt
## 85 0 19 182 2 0 0 0 1 0 2523
## 86 0 33 155 3 0 0 0 0 3 2551
## 87 0 20 105 1 1 0 0 0 1 2557
## 88 0 21 108 1 1 0 0 1 2 2594
## 89 0 18 107 1 1 0 0 1 0 2600
## 91 0 21 124 3 0 0 0 0 0 2622
# 再透過coding檢視對於race進行分類後之差異
c("White", "Black", "Other")[birthwt$race]
## [1] "Black" "Other" "White" "White" "White" "Other" "White" "Other"
## [9] "White" "White" "Other" "Other" "Other" "Other" "White" "White"
## [17] "Black" "White" "Other" "White" "Other" "White" "White" "Other"
## [25] "Other" "White" "White" "White" "Black" "Black" "Black" "White"
## [33] "Black" "White" "Black" "White" "White" "White" "White" "White"
## [41] "Black" "White" "Black" "White" "White" "White" "White" "Other"
## [49] "White" "Other" "White" "Other" "White" "White" "Other" "Other"
## [57] "Other" "Other" "Other" "Other" "Other" "Other" "Other" "White"
## [65] "Other" "Other" "Other" "Other" "White" "Black" "White" "Other"
## [73] "Other" "Black" "White" "Black" "White" "White" "Black" "White"
## [81] "White" "White" "Other" "Other" "Other" "Other" "Other" "White"
## [89] "White" "White" "White" "Other" "White" "White" "White" "White"
## [97] "White" "White" "White" "White" "White" "White" "Other" "White"
## [105] "Other" "Black" "White" "White" "White" "Black" "White" "Other"
## [113] "White" "White" "White" "Other" "White" "Other" "White" "Other"
## [121] "White" "Other" "White" "White" "White" "White" "White" "White"
## [129] "White" "White" "Other" "White" "Black" "Other" "Other" "Other"
## [137] "Other" "Black" "Other" "White" "White" "White" "Other" "Other"
## [145] "White" "White" "Black" "White" "Other" "Other" "Other" "White"
## [153] "White" "White" "White" "Other" "Black" "White" "Black" "Other"
## [161] "White" "Other" "Other" "Other" "Black" "White" "Other" "Other"
## [169] "White" "White" "Black" "Black" "Black" "Other" "Other" "White"
## [177] "White" "White" "White" "Black" "Other" "Other" "White" "Other"
## [185] "White" "Other" "Other" "Black" "White"
# 可觀察對於單一變項做分類後的呈現與取代，對於資料的檢視與管理可產生簡化之作用