问题描述
我在对我的数据运行弗里德曼测试时遇到问题.我正在尝试使用以下命令运行弗里德曼测试:
I'm having trouble running a Friedman test over my data.I'm trying to run a Friedman test using this command:
friedman.test(mean ~ isi | expId, data=monoSum)
在以下数据库(https://www.dropbox.com/s/2ox0y1b4gwld0ai/monoSum.csv):
> monoSum
expId isi N mean
1 m80B1 1 10 100.000000
2 m80B1 2 10 73.999819
3 m80B1 3 10 45.219362
4 m80B1 4 10 116.566174
. . . . .
18 m80L2 2 10 82.945491
19 m80L2 3 10 57.675480
20 m80L2 4 10 207.169277
. . . . . .
25 m80M2 1 10 100.000000
26 m80M2 2 10 49.752687
27 m80M2 3 10 19.042592
28 m80M2 4 10 150.411035
它让我返回错误:
Error in friedman.test.default(c(100, 73.9998193095267, 45.2193621626293, :
not an unreplicated complete block design
我认为它给出了错误,因为当 monoSum$isi==1
的值总是 100.这是正确的吗?
I figure it gives the error because, when monoSum$isi==1
the value of mean is always 100. Is this correct?
然而,monoSum$isi==1
总是 100,因为它是所有其他 monoSum$isi
组在其上标准化的控制组.我不能假设正态分布,所以我不能运行 rmANOVA…有没有办法对这些数据进行弗里德曼测试,还是我在这里遗漏了一个非常重要的点?
However, monoSum$isi==1
is alway 100 because it is the control group on which all the other monoSum$isi
groups are normalized. I can not assume a normal distribution, so I cannot run a rmANOVA…Is there a way to run a friedman test on this data or am I missing a very essential point here?
非常感谢!
推荐答案
如果我运行你的数据集,我没有收到错误:
I don't get an error if I run your dataset:
Friedman rank sum test
data: mean and isi and expId
Friedman chi-squared = 17.9143, df = 3, p-value = 0.0004581
但是,您必须确保将 expId
和 isi
编码为因子.运行这些命令:
However, you have to make sure that expId
and isi
are coded as factors. Run these commands:
monoSum$expID$<-factor(monoSum$expID)
monoSum$isi$<-factor(monoSum$isi)
然后再次运行测试.这对我有类似的问题.
Then run the test again. This has worked for me with a similar problem.
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