作业26

练习1 数据导入

1.将数据集的csv文件导入

mydata <- read.csv("http://stats.idre.ucla.edu/stat/data/binary.csv")

练习2 查看数据

1.查看数据集的第一列

mydata[,1]
  [1] 0 1 1 1 0 1 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 1 1 0
 [42] 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0
 [83] 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0
[124] 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0 0 0 0 1 0
[165] 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 1
[206] 1 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0
[247] 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 0 1 1 1 0 0 1 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 0 1 0 1
[288] 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1
[329] 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 1 0 0 0 0
[370] 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 1 1 1 1 0 0 0 0 0

2.查看数据集的变量名

str(mydata)
'data.frame':    400 obs. of  4 variables:
 $ admit: int  0 1 1 1 0 1 1 0 1 0 ...
 $ gre  : int  380 660 800 640 520 760 560 400 540 700 ...
 $ gpa  : num  3.61 3.67 4 3.19 2.93 3 2.98 3.08 3.39 3.92 ...
 $ rank : Factor w/ 4 levels "1","2","3","4": 3 3 1 4 4 2 1 2 3 2 ...

练习3 数据操作

1.计算数据中各属性的标准差

sapply(mydata, sd)
##   admit     gre     gpa    rank 
##   0.466 115.517   0.381   0.944

2.计算预测值的模型是否比仅仅含有截距的模型（即，空模型）的偏差

with(mylogit, null.deviance - deviance)
## [1] 41.5

练习4 图形绘制操作

1. 画出数据中rank各值出现频数的柱状图

barplot(table(mydata$rank))