1070312Exercise

1.How many items are there in the built-in package MASS? List the names of the item indices from 94 to 103?

library(MASS)
ls("package:MASS")[94:103]

##  [1] "minn38"            "motors"            "muscle"           
##  [4] "mvrnorm"           "nclass.freq"       "neg.bin"          
##  [7] "negative.binomial" "negexp.SSival"     "newcomb"          
## [10] "nlschools"

2.Compute the mean height for the women{datasets} example and substract height from mean to save it as a variable height_centered. Extract regression coefficients as follows:

Why the slope estimates remain the same but the intercept estimates differ?

dta<-women
dta$height_centered <- with(dta,(height-mean(height)))
lm(weight ~ height_centered, data = dta)

## 
## Call:
## lm(formula = weight ~ height_centered, data = dta)
## 
## Coefficients:
##     (Intercept)  height_centered  
##          136.73             3.45

斜率不變，只有截距改變

3.Investigate the side effects of the operator ‘c’ on data frames and on matrices. In particular, explain the difference between c(women) and c(as.matrix(women)).

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

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

c(as.matrix(women))將c(women)轉成矩陣，\(height、\)weight消失

4.Use help to examine the race variable in the birthwt{MASS} dataset. Does it make sense?

> c(“White”, “Black”, “Other”)[birthwt$race]

What happens if you run the above R command?

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

 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"

指令將種族由代碼轉為較易閱讀的文字white、Black、other

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