gap (version 1.2.2)

h2.jags: Heritability estimation based on genomic relationship matrix using JAGS

Description

Heritability estimation based on genomic relationship matrix using JAGS

Usage

h2.jags(y,x,G,eps=0.0001,sigma.p=0,sigma.r=1,parms=c("b","p","r","h2"),...)

Arguments

y

outcome vector

x

covariate matrix

G

genomic relationship matrix

eps

a positive diagonal perturbation to G

sigma.p

initial parameter values

sigma.r

initial parameter values

parms

monitored parmeters

...

parameters passed to jags, e.g., n.chains, n.burnin, n.iter

Value

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

Details

This function performs Bayesian heritability estimation using genomic relationship matrix.

References

Zhao JH, Luan JA, Congdon P (2018). Bayesian linear mixed models with polygenic effects. J Stat Soft 85(6):1-27

Examples

# NOT RUN {
require(gap.datasets)
set.seed(1234567)
meyer <- within(meyer,{
    y[is.na(y)] <- rnorm(length(y[is.na(y)]),mean(y,na.rm=TRUE),sd(y,na.rm=TRUE))
    g1 <- ifelse(generation==1,1,0)
    g2 <- ifelse(generation==2,1,0)
    id <- animal
    animal <- ifelse(!is.na(animal),animal,0)
    dam <- ifelse(!is.na(dam),dam,0)
    sire <- ifelse(!is.na(sire),sire,0)
})
G <- kin.morgan(meyer)$kin.matrix*2
library(regress)
r <- regress(y~-1+g1+g2,~G,data=meyer)
r
with(r,h2G(sigma,sigma.cov))
eps <- 0.001
y <- with(meyer,y)
x <- with(meyer,cbind(g1,g2))
ex <- h2.jags(y,x,G,sigma.p=0.03,sigma.r=0.014)
print(ex)
# }