频数表和列联表的使用

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我是靠谱客的博主体贴大船，最近开发中收集的这篇文章主要介绍频数表和列联表的使用，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

数据使用的是Kock&Edward(1988)，一项风湿性关节炎新疗法的双盲临床试验的结果。

其中Treatment为治疗情况，表示为两种：Treated（用药治疗）、Placebo（安慰剂治疗），improved为改善情况，包括了无改善、一定程度改善和显著改善。

相关函数包括
table()
xtabs()
prop.table()
margin.table()
addmargins()
ftable()

相关实验操作
频数表创建、频数转化比例、二维列联表创建、生成边际频数和比例、添加以及创建边际和、三位列联表创建以及数据的一些处理。

> #引入数据
> library(vcd)
> head(Arthritis)
ID Treatment
Sex Age Improved
1 57
Treated Male
27
Some
2 46
Treated Male
29
None
3 77
Treated Male
30
None
4 17
Treated Male
32
Marked
5 36
Treated Male
46
Marked
6 23
Treated Male
58
Marked
> #一维列联表
> mytable <- with(Arthritis,table(Improved))
> mytable
Improved
None
Some Marked
42
14
28
> #转化为比例值
> prop.table(mytable)
Improved
None
Some
Marked
0.5000000 0.1666667 0.3333333
> #转为百分比
>
> prop.table(mytable)*100
Improved
None
Some
Marked
50.00000 16.66667 33.33333
> #二维列联表
> mytable1 <- xtabs(~ Treatment+Improved,data=Arthritis)
> mytable1
Improved
Treatment None Some Marked
Placebo
29
7
7
Treated
13
7
21
> #行和与行比
> #安慰剂治疗和用药治疗边际频数
> margin.table(mytable1,1)
Treatment
Placebo Treated
43
41
> #安慰剂治疗和用药治疗比例
> prop.table(mytable1,1)
Improved
Treatment
None
Some
Marked
Placebo 0.6744186 0.1627907 0.1627907
Treated 0.3170732 0.1707317 0.5121951
> #列和与列比
> #安慰剂治疗和用药治疗边际频数
> margin.table(mytable1,2)
Improved
None
Some Marked
42
14
28
> #安慰剂治疗和用药治疗比例
> prop.table(mytable1,2)
Improved
Treatment
None
Some
Marked
Placebo 0.6904762 0.5000000 0.2500000
Treated 0.3095238 0.5000000 0.7500000
> #各单元格所占比例
> prop.table(mytable1)
Improved
Treatment
None
Some
Marked
Placebo 0.34523810 0.08333333 0.08333333
Treated 0.15476190 0.08333333 0.25000000
> #添加边际和
>
> addmargins(mytable1)
Improved
Treatment None Some Marked Sum
Placebo
29
7
7
43
Treated
13
7
21
41
Sum
42
14
28
84
> addmargins(prop.table(mytable1))
Improved
Treatment
None
Some
Marked
Sum
Placebo 0.34523810 0.08333333 0.08333333 0.51190476
Treated 0.15476190 0.08333333 0.25000000 0.48809524
Sum
0.50000000 0.16666667 0.33333333 1.00000000
> #使用CrossTable生成二维列联表
> library(gmodels)
> CrossTable(Arthritis$Treatment,Arthritis$Improved)
Cell Contents
|-------------------------|
|
N |
| Chi-square contribution |
|
N / Row Total |
|
N / Col Total |
|
N / Table Total |
|-------------------------|
Total Observations in Table:
84
| Arthritis$Improved
Arthritis$Treatment |
None |
Some |
Marked | Row Total |
--------------------|-----------|-----------|-----------|-----------|
Placebo |
29 |
7 |
7 |
43 |
|
2.616 |
0.004 |
3.752 |
|
|
0.674 |
0.163 |
0.163 |
0.512 |
|
0.690 |
0.500 |
0.250 |
|
|
0.345 |
0.083 |
0.083 |
|
--------------------|-----------|-----------|-----------|-----------|
Treated |
13 |
7 |
21 |
41 |
|
2.744 |
0.004 |
3.935 |
|
|
0.317 |
0.171 |
0.512 |
0.488 |
|
0.310 |
0.500 |
0.750 |
|
|
0.155 |
0.083 |
0.250 |
|
--------------------|-----------|-----------|-----------|-----------|
Column Total |
42 |
14 |
28 |
84 |
|
0.500 |
0.167 |
0.333 |
|
--------------------|-----------|-----------|-----------|-----------|
> #三位列联表
>
> mytable2 <- xtabs(~ Treatment+Sex+Improved,data = Arthritis)
> mytable2
, , Improved = None
Sex
Treatment Female Male
Placebo
19
10
Treated
6
7
, , Improved = Some
Sex
Treatment Female Male
Placebo
7
0
Treated
5
2
, , Improved = Marked
Sex
Treatment Female Male
Placebo
6
1
Treated
16
5
> #三位列联表边际频数
> margin.table(mytable2,1)
Treatment
Placebo Treated
43
41
> margin.table(mytable2,2)
Sex
Female
Male
59
25
> margin.table(mytable2,3)
Improved
None
Some Marked
42
14
28
> #治疗情况和改善情况的边际频数
> margin.table(mytable2,c(1,3))
Improved
Treatment None Some Marked
Placebo
29
7
7
Treated
13
7
21
> #治疗情况和性别各类改善情况比例
> ftable(prop.table(mytable2,c(1,2)))
Improved
None
Some
Marked
Treatment Sex
Placebo
Female
0.59375000 0.21875000 0.18750000
Male
0.90909091 0.00000000 0.09090909
Treated
Female
0.22222222 0.18518519 0.59259259
Male
0.50000000 0.14285714 0.35714286
> ftable(prop.table(mytable2,c(1,2)),3)
Improved
None
Some
Marked
Treatment Sex
Placebo
Female
0.59375000 0.21875000 0.18750000
Male
0.90909091 0.00000000 0.09090909
Treated
Female
0.22222222 0.18518519 0.59259259
Male
0.50000000 0.14285714 0.35714286