BBUM_FDR | R Documentation |
BBUM_FDR
computes the false discovery rate (FDR) value at quantiles
q
(raw p-values) according to the given parameters of the BBUM
distribution.
BBUM_FDR(q, lambda, a, theta, r, dtratio = Inf)
q |
Vector of quantiles. |
lambda |
Vector of BBUM parameter |
a |
Vector of BBUM parameter |
theta |
Vector of BBUM parameter |
r |
Vector of BBUM parameter |
dtratio |
If using the "two-tailed" case of BBUM correction, provide
the ratio of number of data points in the signal class over that in the
background class. Leave as |
This modifed FDR is defined to include primary signal and exclude both secondary signal and null.
If the background assumption is weak, such that a small number
of bona fide hits are anticipated and relevant to the hypothesis at
hand among the data points designated "background class", the FDR could be
made to include the background class. This is akin to a two-tailed test
(despite a one-tailed assumption to begin with). This would allow the
generation of genuine FDR-corrected p values for the background class
points as well. Toggle this using the dtratio
value.
A vector of FDR values at each value of q
.
# Default
BBUM_FDR(q = c(0.001, 0.007, 0.19, 0.5, 0.99),
lambda = 0.65, a = 0.1, theta = 0.02, r = 0.07)
# "Two-tailed"
BBUM_FDR(q = c(0.001, 0.007, 0.19, 0.5, 0.99),
lambda = 0.65, a = 0.1, theta = 0.02, r = 0.07, dtratio = 1.13)
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