Question: (Closed) Same results using fisher.test

0

sugus •

**30**wrote:Hi guys,

I want to use fisher.test() to perform association analysis but I meet a confused problem.

In a test data, different "alternative" will derive to different pValue

```
> fisher.test(c(1,0,1,0,0,0,1,1,1,1,1,1),c(1,0,1,1,1,1,0,0,1,0,0,0))
Fisher's Exact Test for Count Data
data: c(1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1) and c(1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0)
p-value = 0.5455
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.00319446 4.50042353
sample estimates:
odds ratio
0.230358
> fisher.test(c(1,0,1,0,0,0,1,1,1,1,1,1),c(1,0,1,1,1,1,0,0,1,0,0,0),alternative = "less")
Fisher's Exact Test for Count Data
data: c(1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1) and c(1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0)
p-value = 0.2727
alternative hypothesis: true odds ratio is less than 1
95 percent confidence interval:
0.000000 3.151343
sample estimates:
odds ratio
0.230358
```

But in a larger data which is also binary entries with 0 and 1, the pValue is perfectly the same! Why?

```
> as.numeric(x)
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0
[55] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
[109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[163] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[217] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
> as.numeric(y)
[1] 0 0 0 0 1 1 1 0 0 0 0 0 1 1 0 1 0 0 1 0 0 1 1 0 1 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 1
[55] 1 0 1 0 0 1 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0
[109] 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 0 0 0 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0 1 1
[163] 0 0 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 1 1 0 0 0 1 1 1 1 1 0 1 1 0 0 1 0 0 1 1
[217] 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1
> fisher.test(as.numeric(x),as.numeric(y),alternative = "two.side")
Fisher's Exact Test for Count Data
data: as.numeric(x) and as.numeric(y)
p-value = 0.0002977
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.001366782 0.412310161
sample estimates:
odds ratio
0.05916957
> fisher.test(as.numeric(x),as.numeric(y),alternative = "less")
Fisher's Exact Test for Count Data
data: as.numeric(x) and as.numeric(y)
p-value = 0.0002977
alternative hypothesis: true odds ratio is less than 1
95 percent confidence interval:
0.000000 0.337174
sample estimates:
odds ratio
0.05916957
```

Hope somebody could give this an explaination. Thx a lot!

CrossValidated is a better place for your question.

26kHello sugus!

We believe that this post does not fit the main topic of this site.

Not a bioinformatics question.

For this reason we have closed your question. This allows us to keep the site focused on the topics that the community can help with.

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26k