apf_fdr {APFr} | R Documentation |
Implementation of APF and FDR robust estimation
Description
apf_fdr
returns robust estimates of the Average Power Function (APF)
and Bayes False Discovery Rate (FDR) for each value of the threshold Gamma
on the p-value and Tau on the correlation coefficient.
Usage
apf_fdr(data, type = "datf", corr = "spearman", lobs = 0,
seed = 111, gamm = c(1e-04, 0.1, 0.002))
Arguments
data |
Either a vector, matrix or dataframe. |
type |
Set |
corr |
The type of correlation to use when |
lobs |
When |
seed |
A seed (natural number) for the resampling. |
gamm |
The threshold gamma on the p-values to explore (typically |
Value
A list with four elements. A vector APF_gamma
containing the robust
estimates of APF (5th quantiles) for all the gamma values set in gamm
. A vector
FDR_gamma
with the robust estimates of Bayes FDR (95th quantiles). A
vector tau_gamma
with the correlation coefficients tau for each gamma
value explored and another vector with the relative values gamma (gammaval
).
References
Quatto, P, Margaritella, N, et al. Brain networks construction using Bayes FDR and average power function. Stat Methods Med Res. Published online May 14th, 2019; DOI:10.1177/0962280219844288.
Examples
data("Ex1")
APF_lst <- apf_fdr(Ex1,"pvl",lobs=100)
# The example uses the dataset Ex1 (in the APFr package) which is
# a vector of 100 p-values. The number of datapoints used to
# compute each p-value in this example is set to 100. As a result,
# a list of 4 objects is returned.