h2.jags | R Documentation |
Heritability estimation based on genomic relationship matrix using JAGS
h2.jags(
y,
x,
G,
eps = 1e-04,
sigma.p = 0,
sigma.r = 1,
parms = c("b", "p", "r", "h2"),
...
)
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. |
This function performs Bayesian heritability estimation using genomic relationship matrix.
The returned value is a fitted model from jags().
Jing Hua Zhao keywords htest
zhao18gap
## 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)
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