Definition The false discovery rate (FDR) is a statistical approach used in multiple hypothesis testing to correct for multiple comparisons. It is typically used in high-throughput experiments in order to correct for random events that falsely appear significant. When testing a null hypothesis to ...
1.为社么要校正 The multiple comparison problemThe multiple comparison problem potentially arises whenever you would like to test multiple hypotheses simultaneously.If you don't correct for the number of comparisons, then the more hypotheses you test, the higher the probability of obtaining at least ...
1.为社么要校正 The multiple comparison problemThe multiple comparison problem potentially arises whenever you would like to test multiple hypotheses simultaneously.If you don't correct for the number of comparisons, then the more hypotheses you test, the higher the probability of obtaining at least ...
). 我刚才强调的是single test, 在multiple test中, 通常不⽤p-value, ⽽采⽤更加严格的q-value. 与p-value 不同, q-value 控制的是FDR (false discovery rate).3)举个例⼦.假如有⼀种诊断艾滋病的试剂, 试验验证其准确性为99%(每100次诊断就有⼀次false positive). 对于⼀个被检测的⼈(...
Definition The false discovery rate (FDR) is a statistical approach used in multiple hypothesis testing to correct for multiple comparisons. It is typically used in high-throughput experiments in order to correct for random events that falsely appear significant. When testing a null hypothesis to det...
q-value 指的是用 FDR 方法校正后的 p 值,计算方法如下: P.Values <- runif ( 100 ) Q.Values <- p.adjust (P.Values, method = "fdr") References Adjust P-values for Multiple Comparisons False discovery rate Family-wise error rate How does multiple testing correction work?
Supports getting and writing of MINC volumes, running voxel-wise linear models, correlations, etc.; correcting for multiple comparisons using the False Discovery Rate, and more. With contributions from Jason Lerch, Chris Hammill, J… r statistics fdr mixed-models mass-univariate-analysis Updated ...
(i.e., the null hypothesis is actually true for those tests). FDR is generally a somewhat less conservative/more powerful method for correcting for multiple comparisons than procedures like Bonferroni correction that provide strong control of the family-wise error rate (i.e., the probability ...
The multiple comparisons problems encountered in statistical analysis of functional neuroimaging data are well known. As an alternative to conservative methods for Family Wise Error Rate correction (e.g., Random Field Theory (RFT), Bonferonni tests), procedures that control the False Discovery Rate ...
instead of using a p value threshold of 0.05, one would use a stricter threshold of 0.025.The Bonferroni correction is a safeguard against multiple tests of statistical significance on the same data, where 1 out of every 20 hypothesis-tests will appear to be significant at theα= 0.05 level...