fuzzyBHexact: Exact calculation of fuzzy decision rules (Benjamini and...
In fuzzyFDR: Exact calculation of fuzzy decision rules for multiple testing
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
Exact calculation of fuzzy decision rules for multiple
testing. Controls the FDR (false discovery rate) using the
Benjamini and Hochberg method.
Usage
1
fuzzyBHexact(pvals, pprev, alpha = 0.05, tol = 1e-05, q.myuni = T, dp = 20)
Arguments
pvals
observed discrete p-values
pprev
previously attainable p-values under the null distribution
alpha
significance level of the FDR procedure
tol
tolerance for my.match and my.unique
q.myuni
logical. Use my.match instead of match?
dp
no. decimal places to round p-values to
Details
my.match and my.unique may be used instead of match and unique
if there is a problem with calculating the unique set of p-values
(sometimes a problem with very small p-values)
Value
Data frame containing the p-values and previously attainable p-values
input to the function, and the tau (fuzzy decision rule) output. Also
contains the minimum and maximum ranks over allocations for each p-value.
Author(s)
Alex Lewin
References
Kulinsakaya and Lewin (2007).
Examples
1
2
3
4
5
6
Example output
[1] "pvals" "pprev"
[1] "total no. intervals = 8"
[1] "total no. possible alloc. = 1296"
[1] "global sf = 6"
[1] "global sc = 4"
p.minus p.plus r.minus r.plus leng a.minus a.plus
1 0.0000 0.0010 1 2 2 0.0071 0.0143
2 0.0010 0.0039 1 3 3 0.0071 0.0214
3 0.0039 0.0107 2 4 3 0.0143 0.0286
4 0.0107 0.0156 3 5 3 0.0214 0.0357
5 0.0156 0.0352 4 6 3 0.0286 0.0429
6 0.0352 0.0547 5 7 3 0.0357 0.0500
7 0.0547 0.1094 6 7 2 0.0429 0.0500
8 0.1094 0.1445 7 7 1 0.0500 0.0500
[1] "reduced no. intervals = 4"
[1] "reduced no. alloc. = 36"
new.p.minus new.p.plus new.r.minus new.r.plus
1 0.0000 0.0156 1 5
2 0.0156 0.0352 4 6
3 0.0352 0.0547 5 7
4 0.0547 0.1445 6 7
[1] "starting loop over allocations"
[1] ""
[1] "Exact Method"
[1] "alpha = 0.05"
pvals pprev z.min z.max new.z.min new.z.max tau
1 0.0039 0.0000 1 2 1 1 1.0000
2 0.0107 0.0010 2 3 1 1 1.0000
3 0.0156 0.0000 1 4 1 1 1.0000
4 0.0352 0.0039 3 5 1 2 0.9340
5 0.0547 0.0107 4 6 1 3 0.6325
6 0.1094 0.0156 5 7 2 4 0.2815
7 0.1445 0.0352 6 8 3 4 0.0801
pvals pprev z.min z.max new.z.min new.z.max tau
1 0.0039 0.0000 1 2 1 1 1.0000
2 0.0107 0.0010 2 3 1 1 1.0000
3 0.0156 0.0000 1 4 1 1 1.0000
4 0.0352 0.0039 3 5 1 2 0.9340
5 0.0547 0.0107 4 6 1 3 0.6325
6 0.1094 0.0156 5 7 2 4 0.2815
7 0.1445 0.0352 6 8 3 4 0.0801
[1] "pvals" "pprev"
[1] "total no. intervals = 4"
[1] "total no. possible alloc. = 1"
[1] "global sf = 2"
[1] "global sc = 1"
p.minus p.plus r.minus r.plus leng a.minus a.plus
1 0.000 0.004 1 1 1 0.005 0.005
2 0.004 0.035 2 4 3 0.010 0.020
3 0.035 0.145 5 6 2 0.025 0.030
4 0.145 0.363 7 10 4 0.035 0.050
[1] "reduced no. intervals = 3"
[1] "reduced no. alloc. = 1"
new.p.minus new.p.plus new.r.minus new.r.plus
1 0.000 0.004 1 1
2 0.004 0.035 2 4
3 0.035 0.363 5 10
[1] "starting loop over allocations"
[1] ""
[1] "Exact Method"
[1] "alpha = 0.05"
pvals pprev z.min z.max new.z.min new.z.max tau
1 0.004 0.000 1 1 1 1 1.0000
2 0.035 0.004 2 2 2 2 0.3349
3 0.035 0.004 2 2 2 2 0.3349
4 0.035 0.004 2 2 2 2 0.3349
5 0.145 0.035 3 3 3 3 0.0000
6 0.145 0.035 3 3 3 3 0.0000
7 0.363 0.145 4 4 3 3 0.0000
8 0.363 0.145 4 4 3 3 0.0000
9 0.363 0.145 4 4 3 3 0.0000
10 0.363 0.145 4 4 3 3 0.0000
pvals pprev z.min z.max new.z.min new.z.max tau
1 0.004 0.000 1 1 1 1 1.0000
2 0.035 0.004 2 2 2 2 0.3349
3 0.035 0.004 2 2 2 2 0.3349
4 0.035 0.004 2 2 2 2 0.3349
5 0.145 0.035 3 3 3 3 0.0000
6 0.145 0.035 3 3 3 3 0.0000
7 0.363 0.145 4 4 3 3 0.0000
8 0.363 0.145 4 4 3 3 0.0000
9 0.363 0.145 4 4 3 3 0.0000
10 0.363 0.145 4 4 3 3 0.0000
fuzzyFDR documentation built on May 2, 2019, 5:14 a.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
Exact calculation of fuzzy decision rules for multiple testing. Controls the FDR (false discovery rate) using the Benjamini and Hochberg method.
Usage
1 | fuzzyBHexact(pvals, pprev, alpha = 0.05, tol = 1e-05, q.myuni = T, dp = 20)
|
Arguments
pvals |
observed discrete p-values |
pprev |
previously attainable p-values under the null distribution |
alpha |
significance level of the FDR procedure |
tol |
tolerance for my.match and my.unique |
q.myuni |
logical. Use my.match instead of match? |
dp |
no. decimal places to round p-values to |
Details
my.match and my.unique may be used instead of match and unique if there is a problem with calculating the unique set of p-values (sometimes a problem with very small p-values)
Value
Data frame containing the p-values and previously attainable p-values input to the function, and the tau (fuzzy decision rule) output. Also contains the minimum and maximum ranks over allocations for each p-value.
Author(s)
Alex Lewin
References
Kulinsakaya and Lewin (2007).
Examples
1 2 3 4 5 6 |
Example output
[1] "pvals" "pprev"
[1] "total no. intervals = 8"
[1] "total no. possible alloc. = 1296"
[1] "global sf = 6"
[1] "global sc = 4"
p.minus p.plus r.minus r.plus leng a.minus a.plus
1 0.0000 0.0010 1 2 2 0.0071 0.0143
2 0.0010 0.0039 1 3 3 0.0071 0.0214
3 0.0039 0.0107 2 4 3 0.0143 0.0286
4 0.0107 0.0156 3 5 3 0.0214 0.0357
5 0.0156 0.0352 4 6 3 0.0286 0.0429
6 0.0352 0.0547 5 7 3 0.0357 0.0500
7 0.0547 0.1094 6 7 2 0.0429 0.0500
8 0.1094 0.1445 7 7 1 0.0500 0.0500
[1] "reduced no. intervals = 4"
[1] "reduced no. alloc. = 36"
new.p.minus new.p.plus new.r.minus new.r.plus
1 0.0000 0.0156 1 5
2 0.0156 0.0352 4 6
3 0.0352 0.0547 5 7
4 0.0547 0.1445 6 7
[1] "starting loop over allocations"
[1] ""
[1] "Exact Method"
[1] "alpha = 0.05"
pvals pprev z.min z.max new.z.min new.z.max tau
1 0.0039 0.0000 1 2 1 1 1.0000
2 0.0107 0.0010 2 3 1 1 1.0000
3 0.0156 0.0000 1 4 1 1 1.0000
4 0.0352 0.0039 3 5 1 2 0.9340
5 0.0547 0.0107 4 6 1 3 0.6325
6 0.1094 0.0156 5 7 2 4 0.2815
7 0.1445 0.0352 6 8 3 4 0.0801
pvals pprev z.min z.max new.z.min new.z.max tau
1 0.0039 0.0000 1 2 1 1 1.0000
2 0.0107 0.0010 2 3 1 1 1.0000
3 0.0156 0.0000 1 4 1 1 1.0000
4 0.0352 0.0039 3 5 1 2 0.9340
5 0.0547 0.0107 4 6 1 3 0.6325
6 0.1094 0.0156 5 7 2 4 0.2815
7 0.1445 0.0352 6 8 3 4 0.0801
[1] "pvals" "pprev"
[1] "total no. intervals = 4"
[1] "total no. possible alloc. = 1"
[1] "global sf = 2"
[1] "global sc = 1"
p.minus p.plus r.minus r.plus leng a.minus a.plus
1 0.000 0.004 1 1 1 0.005 0.005
2 0.004 0.035 2 4 3 0.010 0.020
3 0.035 0.145 5 6 2 0.025 0.030
4 0.145 0.363 7 10 4 0.035 0.050
[1] "reduced no. intervals = 3"
[1] "reduced no. alloc. = 1"
new.p.minus new.p.plus new.r.minus new.r.plus
1 0.000 0.004 1 1
2 0.004 0.035 2 4
3 0.035 0.363 5 10
[1] "starting loop over allocations"
[1] ""
[1] "Exact Method"
[1] "alpha = 0.05"
pvals pprev z.min z.max new.z.min new.z.max tau
1 0.004 0.000 1 1 1 1 1.0000
2 0.035 0.004 2 2 2 2 0.3349
3 0.035 0.004 2 2 2 2 0.3349
4 0.035 0.004 2 2 2 2 0.3349
5 0.145 0.035 3 3 3 3 0.0000
6 0.145 0.035 3 3 3 3 0.0000
7 0.363 0.145 4 4 3 3 0.0000
8 0.363 0.145 4 4 3 3 0.0000
9 0.363 0.145 4 4 3 3 0.0000
10 0.363 0.145 4 4 3 3 0.0000
pvals pprev z.min z.max new.z.min new.z.max tau
1 0.004 0.000 1 1 1 1 1.0000
2 0.035 0.004 2 2 2 2 0.3349
3 0.035 0.004 2 2 2 2 0.3349
4 0.035 0.004 2 2 2 2 0.3349
5 0.145 0.035 3 3 3 3 0.0000
6 0.145 0.035 3 3 3 3 0.0000
7 0.363 0.145 4 4 3 3 0.0000
8 0.363 0.145 4 4 3 3 0.0000
9 0.363 0.145 4 4 3 3 0.0000
10 0.363 0.145 4 4 3 3 0.0000
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