cophescan1.4.2 package

Run the hierarchical mcmc model to infer priors

Simplifying the output obtained from cophe.multitrait, `cophe.single...

Initiate parameters alpha, beta and gamma

Conversion of parameters alpha, beta and gamma to pnk, pak and pck

per.snp.priors

List of priors: pn, pa and pc over all iterations

Plot region Manhattan for a trait highlighting the queried variant

List of posterior probabilities: Hn, Ha and Hc over all iterations

Prepare data for plotting

Proposal distribution

Calculate log priors

logsum

Log sum

Predict cophescan hypothesis for tested associations

adjust_priors

Average of priors: pnk, pak and pck

Average of priors: pnk, pak and pck from list (memory intensive)

Average of posterior probabilities: Hn, Ha and Hc

Average of posterior probabilities: Hn, Ha and Hc from list (memory in...

combine.bf

extract data through Bayes factors

Run cophescan on multiple traits at once

Prepare data for cophe.single

Prepare data for cophe.susie

cophe.single.lbf

Bayesian cophescan analysis using Approximate Bayes Factors

cophe.susie.lbf

Log posterior calculation

run cophe.susie using susie to detect separate signals

Heatmap of multi-trait cophescan results

cophe_plots showing the Ha and Hc of all traits and labelled above the...

The 'cophescan' package.

Extract beta and p-values of queried variant

Calculation of the posterior prob of Hn, Ha and Hc

Estimate the Hc.cutoff for the required FDR

hypothesis.priors

dnorm for alpha

dgamma for beta

dgamma for gamma

Log likelihood calculation

Run the hierarchical Metropolis Hastings model to infer priors

sample alpha

sample beta

sample gamma

print the summary of results from cophescan single or susie

Target distribution