R: Calculates empirical P-values

calcPval {BlockFeST} | R Documentation

## Calculates empirical P-values

### Description

We propose a simple sampling scheme to verify significant outlier loci subject to local adaptation based on the distribution of the α_{I} values observed after the MCMC iterations (approximated through a region-specific normal distribution (N(α_{I})) without using a time consuming reversible jump model for testing the null hypotheses:
1. For each region I, sample a single value x_{I} \sim N(α_{I}) and y_{I} \sim N(α_{I}), resulting in a distribution of sampled values D_{x} and D_{y} across regions.
2. For each region I, increment its counter c_I if y_I is above the q-quantile for D_{x}.
3. repeat (1-2) 1000 times
The empirical P-value for each α_{I} is the number of times the sample x_{I} is greater than the user-defined significance level q (e.g., the 0.95 quantile) divided by the number of iterations (1000 times).

### Usage

    calcPval(BlockFeST.result, q=0.95)

### Arguments

`BlockFeST.result` | an object returned from the function BlockFeST
`q` | quantile

### Value

empirical P-values

### Examples

    snps   <- system.file("extdata", "snps.txt", package="BlockFeST")
    groups <- system.file("extdata", "groups.txt", package="BlockFeST")
    BlockFeST.result <- BlockFeST(input=snps, GROUP=groups, nb=3, runtime=10)
    P <- calcPval(BlockFeST.result)

[Package BlockFeST version 2.0 Index]