Fit linear or generalized linear mixed models incorporating the effects of a pedigree.

1 2 3 4 5 |

`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`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
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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