Adjust significance threshold (alpha) according to FDR (Benjamini & Hochberg)` method in R
3
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
asked Nov 10 '18 at 22:17
DenisDenis
7710
add a comment |
3
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
asked Nov 10 '18 at 22:17
DenisDenis
7710
add a comment |
3
3
3
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
asked Nov 10 '18 at 22:17
DenisDenis
7710
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
asked Nov 10 '18 at 22:17
DenisDenis
7710
asked Nov 10 '18 at 22:17
DenisDenis
7710
asked Nov 10 '18 at 22:17
DenisDenis
7710
asked Nov 10 '18 at 22:17
DenisDenis
7710
asked Nov 10 '18 at 22:17
DenisDenis
7710
7710
add a comment |
add a comment |
1 Answer
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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
answered Nov 11 '18 at 9:06
paoloeusebipaoloeusebi
641413
Thanks for your reply. Unfortunately i was not able to installmutoss. 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 '18 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 '18 at 11:38
Now i got it, thanks! But there is no ``FDR` correctedalphain the result although. While it's probably to some extent easy to find the correctedalphafrom theadaptiveBHRfunction output in comparison top.adjustindeed. In the mentioned above your example adjustedalpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
– Denis
Nov 11 '18 at 16:48
add a comment |
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1 Answer
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1 Answer
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active
oldest
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active
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oldest
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1
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
answered Nov 11 '18 at 9:06
paoloeusebipaoloeusebi
641413
Thanks for your reply. Unfortunately i was not able to installmutoss. 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 '18 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 '18 at 11:38
Now i got it, thanks! But there is no ``FDR` correctedalphain the result although. While it's probably to some extent easy to find the correctedalphafrom theadaptiveBHRfunction output in comparison top.adjustindeed. In the mentioned above your example adjustedalpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
– Denis
Nov 11 '18 at 16:48
add a comment |
1
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
answered Nov 11 '18 at 9:06
paoloeusebipaoloeusebi
641413
Thanks for your reply. Unfortunately i was not able to installmutoss. 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 '18 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 '18 at 11:38
Now i got it, thanks! But there is no ``FDR` correctedalphain the result although. While it's probably to some extent easy to find the correctedalphafrom theadaptiveBHRfunction output in comparison top.adjustindeed. In the mentioned above your example adjustedalpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
– Denis
Nov 11 '18 at 16:48
add a comment |
1
1
1
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
answered Nov 11 '18 at 9:06
paoloeusebipaoloeusebi
641413
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
answered Nov 11 '18 at 9:06
paoloeusebipaoloeusebi
641413
answered Nov 11 '18 at 9:06
paoloeusebipaoloeusebi
641413
answered Nov 11 '18 at 9:06
paoloeusebipaoloeusebi
641413
answered Nov 11 '18 at 9:06
paoloeusebipaoloeusebi
641413
641413
Thanks for your reply. Unfortunately i was not able to installmutoss. 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 '18 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 '18 at 11:38
Now i got it, thanks! But there is no ``FDR` correctedalphain the result although. While it's probably to some extent easy to find the correctedalphafrom theadaptiveBHRfunction output in comparison top.adjustindeed. In the mentioned above your example adjustedalpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
– Denis
Nov 11 '18 at 16:48
add a comment |
Thanks for your reply. Unfortunately i was not able to installmutoss. 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 '18 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 '18 at 11:38
Now i got it, thanks! But there is no ``FDR` correctedalphain the result although. While it's probably to some extent easy to find the correctedalphafrom theadaptiveBHRfunction output in comparison top.adjustindeed. In the mentioned above your example adjustedalpha=(1/20)*0.01, but in case of multiple significant p-values it would not be clear.
– Denis
Nov 11 '18 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 '18 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 '18 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 '18 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 '18 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 '18 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 '18 at 16:48
add a comment |
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