sci审稿返回意见：power calculation

Posted 2023-05-17

参考技术A 前段时间投了一篇纯生信分析的文章，审稿人的意见是统计分析缺少“power calculation”，也就是功效分析。

在假设检验中，当H0为真而拒绝H0接受H1时，被称为一类错误，犯错的改率使用α表示，也就是我们的p.value需要小于的临界值。当H1为真而决绝H1接受H0时，就被称为二类错误，犯错概率用β表示。在我的理解中，1-β就是power calculation

R包“pwr”包含了多种计算功效的方法。参考

https://blog.csdn.net/gdyflxw/article/details/53997995?spm=1001.2101.3001.6650.2&utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-2.pc_relevant_default&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-2.pc_relevant_default&utm_relevant_index=3

pwr包中没有对秩和检验wilcox.test的pwr计算。需要使用其他的包：“rstatix”。具体使用方法参考：

https://www.datanovia.com/en/lessons/wilcoxon-test-in-r/#effect-size

但是这个包仍然不能计算power calculation，只能计算effect size。

The r value varies from 0 to close to 1. The interpretation values for r commonly in published literature are: 0.10 - < 0.3 (small effect), 0.30 - < 0.5 (moderate effect) and >= 0.5 (large effect).（这里的r就是指用函数wilcox_effsize计算出的effect size）。

我在补数据的时候，用于计算功效值的都是melt类型的数据，也就是使用reshape2包的melt函数改造后的窄数据。对于这种窄数据我写了个小函数包装计算功效值的过程。

这种类型的数据就只需要pwr_my(data = data, group = "group",variable = "variable",value ="value" )

如果只有一个变量，没有那么多variable，只需要输入variable=“NULL”或者不输入，默认就是NULL。

如：pwr_my(data = data,value = "pyrscore",group = "pyr_group")

回答审稿人意见

Dear Editor,
We are very grateful to your helpful comments and suggestions. We thank the anonymous
reviewers who provided useful suggestions for improving the quality of our manuscript. I am
pleased to reply to you that we have thoroughly revised our manuscript considering all the
reviewers‘ comments and suggestions, so that the quality of the manuscript has been improved
significantly.
Here, we attached a summary of our major changes in the revision.
1. We have revised to make the manuscript more readable and concise. We xed unclear
statements and some grammatical problems.
2. According to the reviewers‘ suggestions, we have added biological evidence to explain
the relationship between our method and brain science in Introduction(pp.1).
3. We reduced the part of ORB method description and added new materials to describe
advantages of ORB in Visual Processing(pp.2-3).
4. We have revised to clearly state the experiment parameters of our visual process method
in 5.1 Feature Extraction(pp.6).
5. Finally, we have made detailed responses to address the concerns of Reviewer 1 and Reviewer 2, respectively.

Regards,

 

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