h2.jags {gap}

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 = 1e-04,
  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.

Details

This function performs Bayesian heritability estimation using genomic relationship matrix.

Value

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

Author(s)

Jing Hua Zhao keywords htest

References

Zhao JH, Luan JA, Congdon P (2018). “Bayesian Linear Mixed Models with Polygenic Effects.” Journal of Statistical Software, 85(6), 1 - 27. doi:10.18637/jss.v085.i06.

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)

## End(Not run)