多列联系表

最新推荐文章于 2025-09-11 21:03:21 发布

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最新推荐文章于 2025-09-11 21:03:21 发布

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文章标签： python

原文链接：https://my.oschina.net/u/1785519/blog/1563107

2019独角兽企业重金招聘Python工程师标准>>>

table()生成多维列表

> mytable <- xtabs(~ Treatment+Sex+Improved, data=Arthritis)  
> mytable
, , 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

ftable()

生成一种紧凑而吸引人的方式输出多维列联表

> ftable(mytable)    #将mytable作为参数传入fatable()
                 Improved None Some Marked
Treatment Sex                             
Placebo   Female            19    7      6
          Male              10    0      1
Treated   Female             6    5     16
          Male               7    2      5

边际及比例计算

> margin.table(mytable, 1)    #计算第一个变量 Treatment的边际频数（即求个数和）
Treatment
Placebo Treated 
     43      41 
> margin.table(mytable, 2)
Sex
Female   Male 
    59     25 
> margin.table(mytable, 2)
Sex
Female   Male 
    59     25 
> margin.table(mytable, c(1,3))  # 计算Treatment*Improved分组的边际频数
         Improved
Treatment None Some Marked
  Placebo   29    7      7
  Treated   13    7     21
> ftable(prop.table(mytable, c(1,2)))  #计算Treatment*Sex组合中改善情况为None，Some和Marked的比例
                 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(addmargins(prop.table(mytable, c(1, 2)), 3))  #乘以100，得到百分比而不是比例
                 Improved       None       Some     Marked        Sum
Treatment Sex                                                        
Placebo   Female          0.59375000 0.21875000 0.18750000 1.00000000
          Male            0.90909091 0.00000000 0.09090909 1.00000000
Treated   Female          0.22222222 0.18518519 0.59259259 1.00000000
          Male            0.50000000 0.14285714 0.35714286 1.00000000

转载于:https://my.oschina.net/u/1785519/blog/1563107