gap (version 1.5-1)

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 = 1e-04,
  sigma.p = 0,
  sigma.r = 1,
  parms = c("b", "p", "r", "h2"),
  ...
)

Value

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

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.

Author

Jing Hua Zhao keywords htest

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, tools:::Rd_expr_doi("10.18637/jss.v085.i06").

Examples

if (FALSE) {
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)
}