pedigreemm | R Documentation |
Fit linear or generalized linear mixed models incorporating the effects of a pedigree.
pedigreemm(formula, data, family = NULL, REML = TRUE,
pedigree = list(), control = list(),
start = NULL, verbose = FALSE, subset, weights,
na.action, offset, contrasts = NULL, model = TRUE,
x = TRUE, ...)
pedigree |
a named list of |
formula |
as in |
data |
as in |
family |
as in |
REML |
as in |
control |
as in |
start |
as in |
verbose |
as in |
subset |
as in |
weights |
as in |
na.action |
as in |
offset |
as in |
contrasts |
as in |
model |
as in |
x |
as in |
... |
as in |
All arguments to this function are the same as those to the function
lmer
except pedigree
which must be a named list of
pedigree
objects. Each name (frequently there is
only one) must correspond to the name of a grouping factor in a
random-effects term in the formula
. The observed levels
of that factor must be contained in the pedigree. For each pedigree
the (left) Cholesky factor of the
relationship matrix restricted to the observed levels is calculated
using relfactor
and applied to the model matrix for that
term.
a pedigreemm
object.
2010. A.I. Vazquez, D.M. Bates, G.J.M. Rosa, D. Gianola and K.A. Weigel. Technical Note: An R package for fitting generalized linear mixed models in animal breeding. Journal of Animal Science, 88:497-504.
pedigreemm
, pedigree
,
relfactor
.
p1 <- new("pedigree",
sire = as.integer(c(NA,NA,1, 1,4,5)),
dam = as.integer(c(NA,NA,2,NA,3,2)),
label = as.character(1:6))
A<-getA(p1)
cholA<-chol(A)
varU<-0.4; varE<-0.6; rep<-20
n<-rep*6
set.seed(108)
bStar<- rnorm(6, sd=sqrt(varU))
b<-crossprod(as.matrix(cholA),bStar)
ID <- rep(1:6, each=rep)
e0<-rnorm(n, sd=sqrt(varE))
y<-b[ID]+e0
fm1 <- pedigreemm(y ~ (1|ID) , pedigree = list(ID = p1))
table(y01<-ifelse(y<1.3,0,1))
fm2 <- pedigreemm(y01 ~ (1|ID) , pedigree = list(ID = p1), family = 'binomial')
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