chr1swallace

coloc.bayes.3t <- function(df1,snps=setdiff(colnames(df1),response),response="Y",priors=list(rep(1,14)),r2.trim=0.99,thr=0.005,quiet=TRUE) {
    #we consider all models which contain at most 1 snp for each of the three traits
    snps <- unique(snps)
    n.orig <- length(snps)
    if(n.orig<2)
        return(1)
    prep <- prepare.df(df1, snps, r2.trim=r2.trim, dataset=1, quiet=quiet)
    df1 <- prep$df
    snps <- prep$snps
    

chr1swallace

library(nnet)

nsnp <- 3
simdata <- function(n) {
  X <- matrix(pmin(abs(rnorm(n*nsnp)),3),nrow=n)

  p <- rbinom(n, 1, X[,1]/3)
  q <- rbinom(n, 1, X[,nsnp]/3)

  Y <- ifelse(p>0, 1, ifelse(q>0, 2, 0))

chr1swallace

library(ggdendro)
library(ggplot2)
library(reshape)
library(grid)
library(gtable)

## colours, generated by
## library(RColorBrewer)
## rev(brewer.pal(11,name="RdYlBu"))
my.colours <- c("#313695", "#4575B4", "#74ADD1", "#ABD9E9", "#E0F3F8", "#FFFFBF",