BlockFeST (version 2.0)

calcPval: 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 \(\alpha_{I}\) values observed after the MCMC iterations (approximated through a region-specific normal distribution \((N(\alpha_{I}))\) without using a time consuming reversible jump model for testing the null hypotheses: 1. For each region \(I\), sample a single value \(x_{I} \sim N(\alpha_{I})\) and \(y_{I} \sim N(\alpha_{I})\), 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 \(\alpha_{I}\) 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

# NOT RUN {
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)

# }