hwe.jags {gap} | R Documentation

## Hardy-Weinberg equlibrium test for a multiallelic marker using JAGS

### Description

Hardy-Weinberg equilibrium test

### Usage

    hwe.jags(k,n,delta=rep(1/k,k),lambda=0,lambdamu=-1,lambdasd=1,
                  parms=c("p","f","q","theta","lambda"),...)

### Arguments

`k` | number of alleles
`n` | a vector of k(k+1)/2 genotype counts
`delta` | initial parameter values
`lambda` | initial parameter values
`lambdamu` | initial parameter values
`lambdasd` | initial parameter values
`parms` | monitored parmeters
`...` | parameters passed to jags, e.g., n.chains, n.burnin, n.iter

### Details

This function performs Bayesian Hardy-Weinberg equilibrium test, which mirrors `hwe.hardy`, another implementation for highly polymorphic markers when asymptotic results do not hold.

### Value

The returned value is a fitted model from jags().

### References

Wakefield J (2010). Bayesian methods for examining Hardy-Weinberg equilibrium. Biometrics 66:257-265

### Author(s)

Jing Hua Zhao, Jon Wakefield

### See Also

`hwe.hardy`

### Examples

    ## Not run:
    ex1 <- hwe.jags(4,c(5,6,1,7,11,2,8,19,26,15))
    print(ex1)
    ex2 <- hwe.jags(2,c(49,45,6))
    print(ex2)
    ex3 <- hwe.jags(4,c(0,3,1,5,18,1,3,7,5,2),lambda=0.5,lambdamu=-2.95,lambdasd=1.07)
    print(ex3)
    ex4 <- hwe.jags(9,c(1236,120,3,18,0,0,982,55,7,249,32,1,0,12,0,2582,132,20,1162,29,
                        1312,6,0,0,4,0,4,0,2,0,0,0,0,0,0,0,115,5,2,53,1,149,0,0,4),
                    delta=c(1,1,1,1,1,1,1,1,1),lambdamu=-4.65,lambdasd=0.21)
    print(ex4)
    ex5 <- hwe.jags(8,n=c(
             3,
             4, 2,
             2, 2, 2,
             3, 3, 2, 1,
             0, 1, 0, 0, 0,
             0, 0, 0, 0, 0, 1,
             0, 0, 1, 0, 0, 0, 0,
             0, 0, 0, 2, 1, 0, 0, 0))
    print(ex5)

    # Data and code accordining to the following URL,
    # http://darwin.eeb.uconn.edu/eeb348-notes/testing-hardy-weinberg.pdf
    hwe.jags.ABO <- function(n,...)
    {
      hwe <- function() {
         # likelihood
         pi[1] <- p.a*p.a + 2*p.a*p.o
         pi[2] <- 2*p.a*p.b
         pi[3] <- p.b*p.b + 2*p.b*p.o
         pi[4] <- p.o*p.o
         n[1:4] ~ dmulti(pi[],N)
         # priors
         a1 ~ dexp(1)
         b1 ~ dexp(1)
         o1 ~ dexp(1)
         p.a <- a1/(a1 + b1 + o1)
         p.b <- b1/(a1 + b1 + o1)
         p.o <- o1/(a1 + b1 + o1)
      }
      hwd <- function() {
         # likelihood
         pi[1] <- p.a*p.a + f*p.a*(1-p.a) + 2*p.a*p.o*(1-f)
         pi[2] <- 2*p.a*p.b*(1-f)
         pi[3] <- p.b*p.b + f*p.b*(1-p.b) + 2*p.b*p.o*(1-f)
         pi[4] <- p.o*p.o + f*p.o*(1-p.o)
         n[1:4] ~ dmulti(pi[],N)
         # priors
         a1 ~ dexp(1)
         b1 ~ dexp(1)
         o1 ~ dexp(1)
         p.a <- a1/(a1 + b1 + o1)
         p.b <- b1/(a1 + b1 + o1)
         p.o <- o1/(a1 + b1 + o1)
         f ~ dunif(0,1)
      }
      N <- sum(n)
      ABO.hwe <- R2jags::jags(list(n=n,N=N),,c("pi","p.a","p.b","p.o"),hwe,...)
      ABO.hwd <- R2jags::jags(list(n=n,N=N),,c("pi","p.a","p.b","p.o","f"),hwd,...)
      invisible(list(hwe=ABO.hwe,hwd=ABO.hwd))
    }

    hwe.jags.ABO.results <- hwe.jags.ABO(n=c(862, 131, 365, 702))
    hwe.jags.ABO.results

    ## End(Not run)

[Package gap version 1.2.1 Index]