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Contents

1 Multiple Comparisons 覓語
2 Bonferroni correction
3 R 1
4 2
5 谿瑚襭


Multiple Comparisons 覓語 p螳 覲伎

1 Multiple Comparisons 覓語 #

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  • 螻燕蟲 覲襦 ′一 るジ讌 蟆螻 .
  • ′一覓, , 企 .
  • Chi-Square Test or Fisher's Exact Test襯 p螳 蟆曙 企 譟壱 讌 蟆 れ.
  • 蟆 [1 vs 2, 1 vs 3, 2 vs 3] 企蟆 2x2 table Chi-Square Test or Fisher's Exact Test 蟆 れ(れ 觜蟲).
  • 企蟆 3螳襯 蟆覃 1譬 る(蠏覓願れ 覈 蠍郁)螳 3覦 貉れ.

2 Bonferroni correction #

3螳襯 蟆覃 1譬 る(蠏覓願れ 覈 蠍郁)螳 3覦 貉れ覩襦 一 p-value 3 螻燕 p螳 覲伎伎 . れ 襷 襦 襴曙 蟲一 蟲一 讌讌 觜蟲 蟆 蟆曙 1譬 る螳 豕 3覦郁讌 企 朱襦 3 螻燕 p-value襯 覲伎 譴.
願 Bonferroni correction

3 R 1 #

Input = (
  "Food              Raw.p
  Blue_fish         .34
  Bread             .594
  Butter            .212
  Carbohydrates     .384
  Cereals_and_pasta .074
  Dairy_products    .94
  Eggs              .275
  Fats              .696
  Fruit             .269
  Legumes           .341
  Nuts              .06
  Olive_oil         .008
  Potatoes          .569
  Processed_meat    .986
  Proteins           .042
  Red_meat           .251
  Semi-skimmed_milk  .942
  Skimmed_milk       .222
  Sweets             .762
  Total_calories     .001
  Total_meat         .975
  Vegetables         .216
  White_fish         .205
  White_meat         .041
  Whole_milk         .039
  ")

Data = read.table(textConnection(Input),header=TRUE)
Data = Data[order(Data$Raw.p),]
Data$Bonferroni = p.adjust(Data$Raw.p, method = "bonferroni")
Data

蟆郁骸
> Data
                Food Raw.p Bonferroni
20    Total_calories 0.001      0.025          --> 0.001 * nrow(Data) = 0.025
12         Olive_oil 0.008      0.200          --> 0.008 * nrow(Data) = 0.200
25        Whole_milk 0.039      0.975
24        White_meat 0.041      1.000
15          Proteins 0.042      1.000
11              Nuts 0.060      1.000
5  Cereals_and_pasta 0.074      1.000
23        White_fish 0.205      1.000
3             Butter 0.212      1.000
22        Vegetables 0.216      1.000
18      Skimmed_milk 0.222      1.000
16          Red_meat 0.251      1.000
9              Fruit 0.269      1.000
7               Eggs 0.275      1.000
1          Blue_fish 0.340      1.000
10           Legumes 0.341      1.000
4      Carbohydrates 0.384      1.000
13          Potatoes 0.569      1.000
2              Bread 0.594      1.000
8               Fats 0.696      1.000
19            Sweets 0.762      1.000
6     Dairy_products 0.940      1.000
17 Semi-skimmed_milk 0.942      1.000
21        Total_meat 0.975      1.000
14    Processed_meat 0.986      1.000

4 2 #

http://stats.stackexchange.com/questions/50917/two-questions-about-bonferroni-adjustment
set.seed(123)
data<-data.frame(x=rep(letters[1:4], each=5), y=sort(rlnorm(20)))
pairwise.t.test(x=data$y, g=data$x, p.adj="bonf") #see results below:

#  data:  data$y and data$x 
#    a       b       c      
#  b 1.00000 -       -      
#  c 0.38945 1.00000 -      
#  d 8.3e-06 3.5e-05 0.00031

# P value adjustment method: bonferroni 

t.test(y~x, data[data$x=="a" | data$x=="b",])$p.value*6
t.test(y~x, data[data$x=="a" | data$x=="c",])$p.value*6
t.test(y~x, data[data$x=="a" | data$x=="d",])$p.value*6
t.test(y~x, data[data$x=="b" | data$x=="c",])$p.value*6
t.test(y~x, data[data$x=="b" | data$x=="d",])$p.value*6
t.test(y~x, data[data$x=="c" | data$x=="d",])$p.value*6

# a vs. b = 0.0788128848           
# a vs. c = 0.0001770066           
# a vs. d = 0.0324680659
# b vs. c = 0.0137812904
# b vs. d = 0.0488036762
# c vs. d = 0.0970799045
蠍 蠍郁鍵..
企: : るジ讓曙 襦螻豺 企Ν 譯殊語. 襦螻豺
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