Adjust significance threshold (alpha) according to FDR (Benjamini & Hochberg)` method in R











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I'm aware about p.adjust function in R and it works well for my needs. However, now i'd like to correct significance threshold (alpha) instead of p-values themselves according to FDR (Benjamini & Hochberg) method.
For instance we have a ten of raw p-values:



0.0001,0.001,0.024,0.56,0.0077,0.55,0.0025,0.01,0.015,1


In case of Bonferroni it's very easy:



alpha_Bonferroni_corrected = 0.01/ number of tests (10 in our example)=0.001


But for FDR it will be a more tricky. Is there function in R for that?






r







share|improve this question


























    up vote
    3
    down vote

    favorite












    I'm aware about p.adjust function in R and it works well for my needs. However, now i'd like to correct significance threshold (alpha) instead of p-values themselves according to FDR (Benjamini & Hochberg) method.
    For instance we have a ten of raw p-values:



    0.0001,0.001,0.024,0.56,0.0077,0.55,0.0025,0.01,0.015,1


    In case of Bonferroni it's very easy:



    alpha_Bonferroni_corrected = 0.01/ number of tests (10 in our example)=0.001


    But for FDR it will be a more tricky. Is there function in R for that?






    r







    share|improve this question
























      up vote
      3
      down vote

      favorite









      up vote
      3
      down vote

      favorite











      I'm aware about p.adjust function in R and it works well for my needs. However, now i'd like to correct significance threshold (alpha) instead of p-values themselves according to FDR (Benjamini & Hochberg) method.
      For instance we have a ten of raw p-values:



      0.0001,0.001,0.024,0.56,0.0077,0.55,0.0025,0.01,0.015,1


      In case of Bonferroni it's very easy:



      alpha_Bonferroni_corrected = 0.01/ number of tests (10 in our example)=0.001


      But for FDR it will be a more tricky. Is there function in R for that?






      r







      share|improve this question













      I'm aware about p.adjust function in R and it works well for my needs. However, now i'd like to correct significance threshold (alpha) instead of p-values themselves according to FDR (Benjamini & Hochberg) method.
      For instance we have a ten of raw p-values:



      0.0001,0.001,0.024,0.56,0.0077,0.55,0.0025,0.01,0.015,1


      In case of Bonferroni it's very easy:



      alpha_Bonferroni_corrected = 0.01/ number of tests (10 in our example)=0.001


      But for FDR it will be a more tricky. Is there function in R for that?






      r




      r






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 10 at 22:17









      Denis

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          mutoss package seems to offer greater flexibility



           library(mutoss)
          
 alpha <- 0.01
          
 set.seed(1234)
          
 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
          
 result <- adaptiveBH(p, alpha)
          
 result





          share|improve this answer





















          • Thanks for your reply. Unfortunately i was not able to install mutoss. Error: package or namespace load failed for ‘mutoss’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): there is no package called ‘multtest’. I tried: install.packages('multtest') and got: package ‘multtest’ is not available (for R version 3.4.0)
            – Denis
            Nov 11 at 10:59








          • 1




            Try this if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("multtest", version = "3.8"). bioconductor.org/packages/release/bioc/html/multtest.html
            – paoloeusebi
            Nov 11 at 11:38












          • Now i got it, thanks! But there is no ``FDR` corrected alpha in the result although. While it's probably to some extent easy to find the corrected alpha from the adaptiveBH R function output in comparison to p.adjust indeed. In the mentioned above your example adjusted alpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
            – Denis
            Nov 11 at 16:48











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          up vote
          1
          down vote













          mutoss package seems to offer greater flexibility



           library(mutoss)
          
 alpha <- 0.01
          
 set.seed(1234)
          
 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
          
 result <- adaptiveBH(p, alpha)
          
 result





          share|improve this answer





















          • Thanks for your reply. Unfortunately i was not able to install mutoss. Error: package or namespace load failed for ‘mutoss’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): there is no package called ‘multtest’. I tried: install.packages('multtest') and got: package ‘multtest’ is not available (for R version 3.4.0)
            – Denis
            Nov 11 at 10:59








          • 1




            Try this if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("multtest", version = "3.8"). bioconductor.org/packages/release/bioc/html/multtest.html
            – paoloeusebi
            Nov 11 at 11:38












          • Now i got it, thanks! But there is no ``FDR` corrected alpha in the result although. While it's probably to some extent easy to find the corrected alpha from the adaptiveBH R function output in comparison to p.adjust indeed. In the mentioned above your example adjusted alpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
            – Denis
            Nov 11 at 16:48















          up vote
          1
          down vote













          mutoss package seems to offer greater flexibility



           library(mutoss)
          
 alpha <- 0.01
          
 set.seed(1234)
          
 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
          
 result <- adaptiveBH(p, alpha)
          
 result





          share|improve this answer





















          • Thanks for your reply. Unfortunately i was not able to install mutoss. Error: package or namespace load failed for ‘mutoss’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): there is no package called ‘multtest’. I tried: install.packages('multtest') and got: package ‘multtest’ is not available (for R version 3.4.0)
            – Denis
            Nov 11 at 10:59








          • 1




            Try this if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("multtest", version = "3.8"). bioconductor.org/packages/release/bioc/html/multtest.html
            – paoloeusebi
            Nov 11 at 11:38












          • Now i got it, thanks! But there is no ``FDR` corrected alpha in the result although. While it's probably to some extent easy to find the corrected alpha from the adaptiveBH R function output in comparison to p.adjust indeed. In the mentioned above your example adjusted alpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
            – Denis
            Nov 11 at 16:48













          up vote
          1
          down vote










          up vote
          1
          down vote









          mutoss package seems to offer greater flexibility



           library(mutoss)
          
 alpha <- 0.01
          
 set.seed(1234)
          
 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
          
 result <- adaptiveBH(p, alpha)
          
 result





          share|improve this answer












          mutoss package seems to offer greater flexibility



           library(mutoss)
          
 alpha <- 0.01
          
 set.seed(1234)
          
 p <-c(runif(10, min=0, max=0.01), runif(10, min=0.9, max=1))
          
 result <- adaptiveBH(p, alpha)
          
 result






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 11 at 9:06









          paoloeusebi

          538312




          538312












          • Thanks for your reply. Unfortunately i was not able to install mutoss. Error: package or namespace load failed for ‘mutoss’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): there is no package called ‘multtest’. I tried: install.packages('multtest') and got: package ‘multtest’ is not available (for R version 3.4.0)
            – Denis
            Nov 11 at 10:59








          • 1




            Try this if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("multtest", version = "3.8"). bioconductor.org/packages/release/bioc/html/multtest.html
            – paoloeusebi
            Nov 11 at 11:38












          • Now i got it, thanks! But there is no ``FDR` corrected alpha in the result although. While it's probably to some extent easy to find the corrected alpha from the adaptiveBH R function output in comparison to p.adjust indeed. In the mentioned above your example adjusted alpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
            – Denis
            Nov 11 at 16:48


















          • Thanks for your reply. Unfortunately i was not able to install mutoss. Error: package or namespace load failed for ‘mutoss’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): there is no package called ‘multtest’. I tried: install.packages('multtest') and got: package ‘multtest’ is not available (for R version 3.4.0)
            – Denis
            Nov 11 at 10:59








          • 1




            Try this if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("multtest", version = "3.8"). bioconductor.org/packages/release/bioc/html/multtest.html
            – paoloeusebi
            Nov 11 at 11:38












          • Now i got it, thanks! But there is no ``FDR` corrected alpha in the result although. While it's probably to some extent easy to find the corrected alpha from the adaptiveBH R function output in comparison to p.adjust indeed. In the mentioned above your example adjusted alpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
            – Denis
            Nov 11 at 16:48
















          Thanks for your reply. Unfortunately i was not able to install mutoss. Error: package or namespace load failed for ‘mutoss’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): there is no package called ‘multtest’. I tried: install.packages('multtest') and got: package ‘multtest’ is not available (for R version 3.4.0)
          – Denis
          Nov 11 at 10:59






          Thanks for your reply. Unfortunately i was not able to install mutoss. Error: package or namespace load failed for ‘mutoss’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): there is no package called ‘multtest’. I tried: install.packages('multtest') and got: package ‘multtest’ is not available (for R version 3.4.0)
          – Denis
          Nov 11 at 10:59






          1




          1




          Try this if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("multtest", version = "3.8"). bioconductor.org/packages/release/bioc/html/multtest.html
          – paoloeusebi
          Nov 11 at 11:38






          Try this if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("multtest", version = "3.8"). bioconductor.org/packages/release/bioc/html/multtest.html
          – paoloeusebi
          Nov 11 at 11:38














          Now i got it, thanks! But there is no ``FDR` corrected alpha in the result although. While it's probably to some extent easy to find the corrected alpha from the adaptiveBH R function output in comparison to p.adjust indeed. In the mentioned above your example adjusted alpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
          – Denis
          Nov 11 at 16:48




          Now i got it, thanks! But there is no ``FDR` corrected alpha in the result although. While it's probably to some extent easy to find the corrected alpha from the adaptiveBH R function output in comparison to p.adjust indeed. In the mentioned above your example adjusted alpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
          – Denis
          Nov 11 at 16:48


















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