| hwe.jags {gap} | R Documentation |
Hardy-Weinberg equilibrium test.
hwe.jags(
k,
n,
delta = rep(1/k, k),
lambda = 0,
lambdamu = -1,
lambdasd = 1,
parms = c("p", "f", "q", "theta", "lambda"),
...
)
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. |
This function performs Bayesian Hardy-Weinberg equilibrium test, which mirrors hwe.hardy,
another implementation for highly polymorphic markers when asymptotic results do not hold.
The returned value is a fitted model from jags().
Jing Hua Zhao, Jon Wakefield
Wakefield J (2010). Bayesian methods for examining Hardy-Weinberg equilibrium. Biometrics 66:257-265
## 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)