apf_fdr: Implementation of APF and FDR robust estimation In APFr: Multiple Testing Approach using Average Power Function (APF) and Bayes 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

 ```1 2``` ```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 ≤ 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

 ```1 2 3 4 5 6``` ```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. ```

APFr documentation built on June 18, 2019, 5:05 p.m.