APFr (version 1.0.2)

apf_fdr: 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 "datf" if data is a matrix or dataframe containing the raw data (columns); "pvl" for a vector of p-values.

corr

The type of correlation to use when type = "datf". It can be set to either "spearman" or "pearson".

lobs

When type = "pvl", it indicates the number of datapoints used to compute the correlations.

seed

A seed (natural number) for the resampling.

gamm

The threshold gamma on the p-values to explore (typically \(\le\) 0.05 or 0.1). A min, max and step length value need to be set.

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

# NOT RUN {
data("Ex1")
APF_lst <- apf_fdr(Ex1,"pvl",lobs=100)
# }
# NOT RUN {
# 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.
# }
# NOT RUN {
# }